1393 research outputs found
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E-Democratic Government Success Framework for United States’ Municipalities
Get PDFThis project develops a comprehensive E-Democratic Government Success Framework that addresses low citizen engagement in local US politics. To develop this framework, I consult the literature on democratic participation, socio-technical theory, data security and privacy, decision support systems, and design science methodology. The main contribution of this project is a five-part method artifact for implementing E-Democracy initiatives—something that has not been readily attempted, despite the decentralized nature of US democracy and the opportunities it offers to experiment with institutions and deliberative procedures. This artifact gives policymakers the means to design, implement, adopt, and evaluate E-Democracy services; and it gives citizens and third parties, such as independent watchdogs, the ability to evaluate E-Democracy initiatives. Additionally, it contributes to the growing research agenda that considers the integration of information communication technology (ICT) into the policymaking process. To evaluate the effectiveness of this artifact, I use three methods: (1) benchmarking through a comparative gap analysis of the artifact’s requirements, past E-Democracy initiatives in the United States, and cybersecurity frameworks; (2) scenario creation that considers the artifact’s application through a synthetic lawsourcing instantiation; and (3) application of defense in depth methodology through mapping artifact requirements that overlap
Toward extracting the scattering phase shift from integrated correlation functions
No full textIn present work, a relation that connects the integrated correlation function of a trapped two-particle system to infinite volume particles scattering phase shift is derived. It has the potential to provide an alternative approach for extracting two-particle scattering phase shift from integrated correlation function in lattice simulation at small Euclidean time region. Both (i) perturbation calculation of (1 þ 1)-dimensional lattice Euclidean field theory model of fermions interacting with a contact interaction and (ii) Monte Carlo simulation of a 1D exactly solvable quantum mechanics model are carried out to test the proposed relation. In contrast to conventional two-step approach of extracting energy levels from temporal correlation function in lattice simulation at large Euclidean time first and then applying Lüscher formula to convert energy levels into scattering phase shifts, we show that the difference of integrated correlation functions between interacting and noninteracting trapped systems converges rapidly to infinite volume limit that is given in terms of scattering phase shifts at small Euclidean time region
Data integration with diverse data: aerospace industry insights from a systematic literature review
Get PDFData Integration (DI) is a technical framework that facilitates the integration of different data sources into a unified system. Often, organizational data is spread across various files, databases, and personal computers, resulting in data silos and difficulties in accessing vital organizational knowledge. The solution rests in the development of Information Systems capable of handling diverse data from multiple heterogeneous sources. This study presents a systematic literature review on data integration approaches, specifically focusing on the aerospace industry. We conducted a literature search on IEEE Xplore database with keywords ‘data integration aerospace’ for articles published between January 2000 and December 2022 to discover aerospace industry insights on data integration with diverse data. Four (4) distinct aerospace-specific data categories and three (3) data integration types were discovered. The findings of this research offer insights on data integration in the aerospace domain with the results suggesting limited research on cloud-based integration
Design Principles For App-Based Healthcare Interventions: A Mixed Method Approach
Get PDFDespite the ubiquity of mobile health applications (apps), the practical use and success of the apps have been questionable. Design Principles (DP) can affect chronic health app user satisfaction and have been studied for ensuring favorable app usage. However, there is no consensual definition of DP within the preceding literature, which has a technical rather than an end-user-centric focus and lacks a rigorous theoretical basis. Moreover, different levels of DPs’ application can lead to differential user satisfaction as influenced by the user-contextual environment, warranting a quantitative assessment. Accordingly, the overarching question to be addressed is which DP for the self-management of chronic conditions contributes to better user satisfaction outcomes. The research focuses on Multiple Sclerosis (MS) as a representative condition. This research uses a mixed methods, with a qualitative approach for DP identification and a quantitative approach for the studying the DP-Satisfaction relationship. The DP identification is achieved through - 1) An in depth review of foundational theory for greater validity, 2) A Systematic Literature Review (SLR), for DP themes grounded in theory, and 3) Manually coded user reviews for MS apps. The theoretical underpinnings of the empirical approach are established through a composite theoretical lens, based on technologically, behaviorally, and cognitively oriented frameworks. The DP extracted from theory, SLR, and manual coding methods are found to be largely consistent with each other, namely ‘Communication with Clinicians’, ‘Compatibility, ‘Education’, ‘Notifications’, ‘Tracking’, ‘Social Support’, ‘Ease of Use’, ‘Technical Support’, ‘Usefulness’, ‘Privacy and Security’, and Quality. An ordinal logistic regression analysis is conducted to understand the relationship between DP and User Satisfaction outcomes based on the manually coded DP scores of the user reviews. All DP have a significant impact on User Satisfaction. From a theoretical perspective, the research improves our understanding of key design principles for the self-management of chronic conditions such as MS and the impact of such principles on user satisfaction. From a practical perspective, the findings provide guidance to the user requirement elicitation process, potentially leading to the development of more successful, sustainable, and responsive healthcare interventions
In Defense of Artificial Intelligence
Get PDFAI usage in our society is more prevalent today compared to years ago. Our evolution in computing has created problems that never existed before because of a fundamental lack of security-minded development. Security was always a second thought and never a priority. With the prevalence of AI making security a bigger priority, the question arises if AIs such as ChatGPT, Github Co-pilot, Alpha Go, and Bing AI use the best security practices when implemented. The creation of modern technologies such as phones, the Internet, and computers have all seen horrendous security concerns addressed later on in development. With how technology currently stands, there is always innovation, but security research consistently falls behind or is not considered. The world is always changing and finding new vulnerabilities constantly, especially with the speed of AI’s current development, which does not definitively address these security concerns. This project will identify different points of the development process where AI is susceptible to attacks or has vulnerabilities and create a framework to mitigate these concerns. The framework will also help developers enumerate these vulnerabilities and identify security fixes or considerations when developing AI. This proposed framework aims to help developers build and maintain more secure AI throughout the implementation process. This foundational knowledge will also help researchers in the field, as it establishes points to investigate for future projects
The Well-Being of a University: The Relationship Between Gratitude and Organizational Commitment on Faculty Members\u27 Intention to Stay
Get PDFRetaining good employees can be difficult , and no organization is immune. This study investigated the relationship between gratitude and organizational commitment to determine if they can predict the intention to stay among university faculty. Specifically, this study analyzed gratitude, a construct that has not been researched much in the workplace. There was a significant relationship between gratitude on organizational commitment. Gratitude and organizational commitment were important predictors of intention to stay. Gratitude was supported as a moderator between organizational commitment and intention to stay. Organizations should incorporate posititve reinforcements to express gratitude to increase organizational commitment and intent to stay
Measuring the Performance Cost of Manual System Call Detections Via Process Instrumentation Callback (PIC)
Get PDFThis quasi-experimental before-and-after study measured the performance impact of using Process Instrumentation Callback (PIC) to detect the use of manual system calls on the Windows operating system. The Windows Application Programming Interface (WinAPI), the impacts of system call monitoring, and the limitations of current detection mechanisms and their downsides were reviewed in-depth. Previous literature was evaluated that identified PIC as a unique solution to monitor system calls entirely from User-Mode, being able to rely on the Windows Kernel to intercept a target process. Unlike previous monitoring techniques, PIC must handle all system calls when performing analysis which requires an increase in processing. The impact on a single process was evaluated by recording CPU time, memory utilization, and clock time. Three different iterations that performed additional analysis were developed and tested to determine the cost of increased fidelity in detection. Results showed a statistically significant increase when PIC was applied in each version. However, the rate of impact was drastically reduced by restricting dynamic lookups to process initialization and the elimination of the Microsoft Debugging Engine. Future integration with existing detection mechanisms such as User-Mode hooks and Event-Tracing for Windows is encouraged and discussed
A Novel Approach To A Second Channel Authentication Method Utilizing Alphanumerical Data Transfer Through Audio Waves
Get PDFMalware Pattern of Life Analysis
Get PDFMany malware classifications include viruses, worms, trojans, ransomware, bots, adware, spyware, rootkits, file-less downloaders, malvertising, and many more. Each type may share unique behavioral characteristics with its methods of operations (MO), a pattern of behavior so distinctive that it could be recognized as having the same creator. The research shows the extraction of malware methods of operation using the step-by-step process of Artificial-Based Intelligence (ABI) with built-in Density-based spatial clustering of applications with noise (DBSCAN) machine learning to quantify the actions for their similarities, differences, baseline behaviors, and anomalies. The collected data of the research is from the ransomware sample repositories of Malware Bazaar and Virus Share, totaling 1300 live malicious codes ingested into the CAPEv2 malware sandbox, allowing the capture of traces of static, dynamic, and network behavior features. The ransomware features have shown significant activity of varying identified functions used in encryption, file application programming interface (API), and network function calls. During the machine learning categorization phase, there are eight identified clusters that have similar and different features regarding function-call sequencing events and file access manipulation for dropping file notes and writing encryption. Having compared all the clusters using a “supervenn” pictorial diagram, the characteristics of the static and dynamic behavior of the ransomware give the initial baselines for comparison with other variants that may have been added to the collected data for intelligence gathering. The findings provide a novel practical approach for intelligence gathering to address ransomware or any other malware variants’ activity patterns to discern similarities, anomalies, and differences between malware actions under study