Creating a Culture of Philanthropy in Nonprofit Arts Organizations

This paper explores a growing theory known as a culture of philanthropy through the lens of a nonprofit arts organization. A culture of philanthropy refers to an organization’s attitude toward philanthropy, fund development, and the effort to create a community of donor inclusion which can have a lasting effect on the organization and the community well beyond financial growth. Arts organizations are exploring radical innovative methods in order to create a culture of funders, continuous patronage, and community engagement. This paper also discusses the concept of venture philanthropy and its efforts to change the relationship between funders and grantees from dependency to partnership, and how this affects funding for arts organizations. With shifts in funding, the growing competition for grants and private donors, and the declining funds for the arts from the private sector, it is more important than ever for arts organizations to prove their positive impact on the community to the new entrepreneurial, results-oriented philanthropists. Creating a culture of philanthropy is one way to promote positive change and growth within an organization as well as the greater community

Vulnerability anti-patterns:a timeless way to capture poor software practices (Vulnerabilities)

There is a distinct communication gap between the software engineering and cybersecurity communities when it comes to addressing reoccurring security problems, known as vulnerabilities. Many vulnerabilities are caused by software errors that are created by software developers. Insecure software development practices are common due to a variety of factors, which include inefficiencies within existing knowledge transfer mechanisms based on vulnerability databases (VDBs), software developers perceiving security as an afterthought, and lack of consideration of security as part of the software development lifecycle (SDLC). The resulting communication gap also prevents developers and security experts from successfully sharing essential security knowledge. The cybersecurity community makes their expert knowledge available in forms including vulnerability databases such as CAPEC and CWE, and pattern catalogues such as Security Patterns, Attack Patterns, and Software Fault Patterns. However, these sources are not effective at providing software developers with an understanding of how malicious hackers can exploit vulnerabilities in the software systems they create. As developers are familiar with pattern-based approaches, this paper proposes the use of Vulnerability Anti-Patterns (VAP) to transfer usable vulnerability knowledge to developers, bridging the communication gap between security experts and software developers. The primary contribution of this paper is twofold: (1) it proposes a new pattern template – Vulnerability Anti-Pattern – that uses anti-patterns rather than patterns to capture and communicate knowledge of existing vulnerabilities, and (2) it proposes a catalogue of Vulnerability Anti-Patterns (VAP) based on the most commonly occurring vulnerabilities that software developers can use to learn how malicious hackers can exploit errors in software

Using multiple GPUs to accelerate string searching for digital forensic analysis

String searching within a large corpus of data is an important component of digital forensic (DF) analysis techniques such as file carving. The continuing increase in capacity of consumer storage devices requires corresponding im-provements to the performance of string searching techniques. As string search-ing is a trivially-parallelisable problem, GPGPU approaches are a natural fit – but previous studies have found that local storage presents an insurmountable performance bottleneck. We show that this need not be the case with modern hardware, and demonstrate substantial performance improvements from the use of single and multiple GPUs when searching for strings within a typical forensic disk image

Doctor of Philosophy

dissertationThe human brain is the seat of cognition and behavior. Understanding the brain mechanistically is essential for appreciating its linkages with cognitive processes and behavioral outcomes in humans. Mechanisms of brain function categorically represent rich and widely under-investigated biological substrates for neural-driven studies of psychiatry and mental health. Research examining intrinsic connectivity patterns across whole brain systems utilizes functional magnetic resonance imaging (fMRI) to trace spontaneous fluctuations in blood oxygen-level dependent (BOLD) signals. In the first study presented, we reveal patterns of dynamic attractors in resting state functional connectivity data corresponding to well-documented biological networks. We introduce a novel simulation for whole brain dynamics that can be adapted to either group-level analysis or single-subject level models. We describe stability of intrinsic functional architecture in terms of transient and global steady states resembling biological networks. In the second study, we demonstrate plasticity in functional connectivity following a minimum six-week intervention to train cognitive performance in a speed reading task. Long-term modulation of connectivity with language regions indicate functional connectivity as a candidate biomarker for tracking and measuring functional changes in neural systems as outcomes of cognitive training. The third study demonstrates utility of functional biomarkers in predicting individual differences in behavioral and cognitive features. We successfully predict three major domains of personality psychologyintelligence, agreeableness, and conscientiousnessin individual subjects using a large (N=475) open source data sample compiled by the National Institutes of Healths Human Connectome Project

Excluding Two Minors of the Petersen Graph

In this dissertation, we begin with a brief survey of the Petersen graph and its role in graph theory. We will then develop an alternative decomposition to clique sums for 3-connected graphs, called T-sums. This decomposition will be used in Chapter 2 to completely characterize those graphs which have no P_3 minor, where P_3 is a graph with 7 vertices, 12 edges, and is isomorphic to the graph created by contracting three edges of a perfect matching of the Petersen Graph. In Chapter 3, we determine the structure of any large internally 4-connected graph which has no P_2 minor, where P_2 is a graph on 8 vertices, 13 edges, and is isomorphic to the graph created by contracting two edges of a perfect matching of the Petersen Graph

F22RS SGR 8 (Building Renaming Committee)

A resolution to strongly condemn President William F. Tate IV for actions taken in regards to the Building Name Evaluation Committee and to Urge and Request LSU to follow-through on Fall 2017’s SGR 15 and Fall 2020’s SGR

Topic-dependent sentiment analysis of financial blogs

While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches

Exploring the use of paragraph-level annotations for sentiment analysis of financial blogs

In this paper we describe our work in the area of topic-based sentiment analysis in the domain of financial blogs. We explore the use of paragraph-level and document-level annotations, examining how additional information from paragraph-level annotations can be used to increase the accuracy of document-level sentiment classification. We acknowledge the additional effort required to provide these paragraph-level annotations, and so we compare these findings against an automatic means of generating topic-specific sub-documents