Social media is impacting the practice of public relations inmany different ways, but the focus of this dissertation is on the power of big data from social media to identify and assess the relationship that stakeholders have with the organization. Social media analytics have tended to measure reactions to messages, rather than the strength of the relationship, even though public relations is responsible for building strong relationships with the organization’s stakeholders. Yet, social media provides insight into the conversations that stakeholders have with other stakeholders about the organization and thus can reveal insight into the quality of the relationship they have with the organization.<div><br></div><div>This dissertation takes a networked approach to understandthe strength of the relationship that the organization has with its stakeholders, meaning it acknowledges that the relationships two entities have with each other are influenced by the relationships those entities have with others in common. In this case, the relationship that a stakeholder has with the organizationis influenced by the relationship the stakeholder has with other stakeholders. Thus, one way to study the relationship that a stakeholder has with the organization is to look at the conversation and the postings on social media among the various stakeholders. The ultimate aim of the dissertation is to show how the relationship can be assessed, so the organization can create strategies that develop mutually beneficial relationships over time.<br></div><div><br></div><div>The context for the study is based on two major events where companies deliberately gather together their stakeholders to interact in person and onsocial media about issues and products related to the organization’sfuture. The first event is Adobe Creative Max, which Adobe hosts each year for creative professionals. The second context for the study is Dreamforce, which is hosted by Salesforce.com and includes so many attendees that the company has to bring in cruise ships to dock in the San Francisco Bay during the event since all the hotels in the area sell out far in advance. These two events provide a specific situation where stakeholders interact with other stakeholders outside of a crisis, which represents the majority of day-to-day public relations practice. Twitter data was collected during for each week of each conference, and all company tweets were filtered out of the data sample. Atext-mining approach was then used to examine the conversations among the stakeholders at the events.<br></div><div><br></div><div>Findings indicate that the strongest relationship was developed by Salesforce.com with its stakeholders at the Dreamforce 2018 event in large part because ofthe CEO’s keynote andthe organizational commitment to social justice and sustainability. Granted, Salesforce hadalready worked to develop a culture among employees and customers based on the concept, “family,”or “Ohana.” However, the text of the conversations reveal that the focus at this conference was on societal issues presented by the CEO. In contrast, the findings from the Adobe conference suggest the organization has a transactional relationship with its stakeholders, in part because the CEO keynote focused heavily on products and technology. The implications of these findings indicate that big data from social media can be used to assess relationships, especially when social media data represents conversations and interactions among stakeholders. The findings also show the influence of CEO communications on the relationship and the vital role that public relations practitioners play in setting that CEO communications agenda.</div>

This research investigates the complexities of artistic authorship under the production conditions of the web. It is driven by a fascination with the possibilities of expanding the authorial sphere of the artwork to include the productive capacities of other subjectivities, entities and processes. I offer the neologism ‘extra-subjectivity’ to reflect on this emerging form of production, in which the ultimate manifestation of the artwork often exceeds the author’s intentions. As well as the written thesis, it comprises seven artworks that represent a distinctive approach characterized by playfulness, humour and the use of generative computational processes. Several early works explore my authorial agency in relation to algorithmically generated variations of texts, including William Blake’s poem The Fly and the song Puff the Magic Dragon. Later, algorithmic generation is combined with the appropriation of content shared on social media, as in Infinite Violets, which displays variations of a Shakespearean verse along with images from Flickr. I draw on digital sociological methods to create a hybrid approach in which the web is understood as an evolving medium made up of digital objects and devices that can be repurposed for art practice. This approach underpins 'Flickr Nude or Noodle Descending a Staircase', which uses images programmatically accessed through Flickr’s application programming interface to remake a Marcel Duchamp painting for the web. 'Selfie Portrait' displays Instagram photographs tagged with ‘Selfie’ alongside users’ biographical information, which drives the ‘Copyright Episode’, an extended account of the legal contexts surrounding web-based art practices. Here, I demonstrate how such practices are entangled socially, ethically and legally with the distinct production conditions of the web. I argue that authorship is a question of responsibility as well as ‘ownership’, which is why ethics are as important as the law.

This creative non-thesis project includes three theories from communication studies. Uncertainty Reduction Theory, Cultivation Theory, and Cognitive Dissonance Theory. Each theoretical framework also includes a script written with the intent of filming in the future. Those videos could be shown in communication classes. These three theories cover a breadth of knowledge in the field as they pertain to interpersonal communication, media studies, and persuasion.

<p>Human action recognition continues to evolve and is examined better using deep learning techniques. Several successes have been recorded in the field of action recognition but only very few has focused on dance. This is because dance actions and, especially Traditional African dance, are long and involve fast movement of body parts. This research proposes a novel framework that applies data science algorithms to the field of cultural preservation by applying various deep learning techniques to identify, classify and model Traditional African dances from videos. Traditional African dances are important part of the African culture and heritage. Digital preservation of these dances in their myriad forms is a problem. The dance dataset was constituted using freely available YouTube videos. Three Traditional African dances – Adowa, Bata and Swange – were used for the dance classification process. Two Convolutional Neural Network (CNN) models were used for the classification and they achieved an accuracy of 97% and 98% respectively. Sound classification of Adowa, Bata and Swange drum ensembles were also carried out; an accuracy of 96% was achieved. Human Pose Estimation Algorithms were applied to the Sinte dance. A model of Sinte dance, which can be exported to other environments, was obtained.</p>

This study proposes a social and emotional disclosure model for understanding the mechanism that explains sharing intimate information on social media (Twitter). Previous research has indicated that some aspects of social, emotional, and attitudinal information processing are involved in disclosure of intimate information. However, these factors have been considered in isolation. This study proposes and tests a theoretically grounded model that brings all of these factors together by combining individual and group social media behaviors and online information processing in the realm of online social movements. The core explanatory model considers the impact of peer response, emotional evaluation, personal relevance, issue orientation, and motivation to post online on intimate information disclosure online. A path analysis building on four Poisson multiple regressions conducted on 28,629 #MeToo tweets evaluates the relationships proposed in the explanatory model. Results indicate that emotional evaluation and motivation to post online have direct, positive impacts on online disclosure. Other factors such as peer response, issue orientation, and personal relevance have negative direct relationships with online disclosure. Motivation to post online mediates the effects of emotional evaluation, issue orientation, and personal relevance on online disclosure while issue orientation mediates the effect of personal relevance on motivation to post online. This study offers findings that have use for practitioners interested in hashtag virality and to social media users interested in social influence and online information sharing.

<div>Counter Unmanned Aerial System (C-UAS) development and fielding has greatly accelerated over the last several years to protect against all classes of Unmanned Aerial System (UAS) threats. Integration of the detection and tracking systems, the engagement systems, and other portions of the kill chain including command and control (C2) is ongoing. A significant concern is that the majority of these developments are designed for defending ships at sea. Most of these technological advances cannot be used within restricted waters or in port, foreign or domestic, due to the potential of high collateral damages and the fact that they are not currently readily available for dissemination to the Fleet.</div><div>The problem addressed by this project is to determine how to defend high value units from the threat of weaponized UAVs while moored pier-side with currently in-place weapons systems. This study will take a parameter-driven approach based on existing technologies to determine if an enhanced fire-control system integrated with standard issue weaponry can increase watchstander accuracy required to safely defend a high value unit pierside.</div>

In recent years, there has been a rapid growth in deployment and usage of realtime network applications, such as Voice-over-IP, video calls/video conferencing, live network seminars, and networked gaming. The continued increase in the popularity of realtime applications requires a more intense focus on the provision of strict guarantees for Quality of Service (QoS) parameters such as delay, jitter and packet loss in access networks. At the same time, wireless networking technologies have become increasingly popular with a wide array of devices such as laptop computers, Personal Digital Assistants (PDAs), and cellular phones being sold with built-in WiFi and WiMAX interfaces. For realtime applications to be popular over wireless networks, simple, robust and effective QoS mechanisms suited for a variety of heterogeneous wireless networks must be devised. Implementing the same QoS mechanisms across multiple neighbouring networks aids seamless handover by ensuring that a flow will be treated in the same way, both before and after handover. To provide guaranteed QoS, an access network should limit load using an admission control algorithm. In this research, we propose a method to provide effective admission control for variable bit rate realtime flows, based on the Central Limit Theorem. Our objective is to estimate the percentage of packets that will be delayed beyond a predefined delay threshold, based on the mean and variance of all the flows in the system. Any flow that will increase the percentage of delayed packets beyond an acceptable threshold can then be rejected. Using simulations we have shown that the proposed method provides a very effective control of the total system load, guaranteeing the QoS for a set of accepted flows with negligible reductions in the system throughput. To ensure that flow data is transmitted according to the QoS requirements of a flow, a scheduling algorithm must handle data intelligently. We propose methods to allow more efficient scheduling by utilising existing Medium Access Control mechanisms to exchange flow information. We also propose a method to determine the delay-dependent "value" of a packet based on the QoS requirements of the flow. Using this value in scheduling is shown to increase the number of packets sent before a predetermined deadline. We propose a measure of fairness in scheduling that is calculated according to how well each flow's QoS requirements are met. We then introduce a novel scheduling paradigm, Delay Loss Controlled-Earliest Deadline First (DLC-EDF), which is shown to provide better QoS for all flows compared to other scheduling mechanisms studied. We then study the performance of our admission control and scheduling methods working together, and propose a feedback mechanism that allows the admission control threshold to be tuned to maximise the efficient usage of available bandwidth in the network, while ensuring that the QoS requirements of all realtime flows are met. We also examine heterogeneous/vertical handover, providing an overview of the technologies supporting seamless handover. The issues studied in this area include a method of using the Signal to Noise Ratio to trigger handover in heterogeneous networks and QoS Mapping between heterogeneous networks. Our proposed method of QoS mapping establishes the minimum set of QoS parameters applicable to individual flows, and then maps these parameters into system parameter formats for both 802.11e and 802.16e networks.

Multipath propagation and mobility in wireless communication systems give rise to variations in the amplitude and phase of the transmitted signal, commonly referred to as fading. Many wireless applications are affected by slowly varying fading, where the channel is non-ergodic, leading to non-reliable transmission during bad channel realizations. These communication scenarios are well modeled by the block-fading channel, where the reliability is quantatively characterized by the outage probability. This thesis focuses on the analysis and design of adaptive transmission schemes to improve the outage performance of both single- and multiple-antenna transmission over the block-fading channel, especially for the cases where discrete input constellations are used. Firstly, a new lower bound on the outage probability of non-adaptive transmission is proposed, providing an efficient tool for evaluating the performance of non-adaptive transmission. The lower bound, together with its asymptotic analysis, is essential for efficiently designing the adaptive transmission schemes considered in the thesis. Secondly, new power allocation rules are derived to minimize the outage probability of fixed-rate transmission over block-fading channels. Asymptotic outage analysis for the resulting schemes is performed, revealing important system design criteria. Furthermore, the thesis proposes novel suboptimal power allocation rules, which enjoy low-complexity while suffering minimal losses as compared to the optimal solution. Thus, these schemes facilitate power adaptation in low-cost devices. Thirdly, the thesis considers incremental-redundancy automatic-repeat-request (INR-ARQ) strategies, which perform adaptive transmission based on receiver feedback. In particular, the thesis concentrates on multi-bit feedback, which has been shown to yield significant gains in performance compared to conventional single-bit ARQ schemes. The thesis proposes a new information-theoretic framework for multi-bit feedback INR-ARQ, whereby the receiver feeds back a quantized version of the accumulated mutual information. Within this framework, the thesis presents an asymptotic analysis which yields the large gains in outage performance offered by multi-bit feedback. Furthermore, the thesis proposes practical design rules, which further illustrates the benefits of multi-bit feedback in INR-ARQ systems. In short, the thesis studies the outage performance of transmission over block-fading channels. Outage analysis is performed for non-adaptive and adaptive transmission. Improvements for the existing adaptive schemes are also proposed, leading to either lower complexity requirements or better outage performance. Still, further research is needed to bring the benefits offered by adaptive transmission into practical systems. / Thesis (PhD)--University of South Australia, 2010

Opportunistic communication techniques have shown to provide significant performance improvements in centralised random access wireless networks. The key mechanism of opportunistic communication is to send back-to-back data packets whenever the channel quality is deemed "good". Recently there have been attempts to introduce opportunistic communication techniques in distributed wireless networks such as wireless ad hoc networks. In line of this research, we propose a new paradigm of medium access control, called Channel MAC based on the channel randomness and opportunistic communication principles. Scheduling in Channel MAC depends on the instance at which the channel quality improves beyond a threshold, while neighbouring nodes are deemed to be silent. Once a node starts transmitting, it will keep transmitting until the channel becomes "bad". We derive an analytical throughput equation of the proposed MAC in a multiple access environment and validate it by simulations. It is observed that Channel MAC outperforms IEEE 802.11 for all probabilities of good channel condition and all numbers of nodes. For higher number of nodes, Channel MAC achieves higher throughput at lower probabilities of good channel condition increasing the operating range. Furthermore, the total throughput of the network grows with increasing number of nodes considering negligible propagation delay in the network. A scalable channel prediction scheme is required to implement the practical Channel MAC protocol in practice. We propose a mean-value based channel prediction scheme, which provides prediction with enough accuracy to be used in the Channel MAC protocol. NS2 simulation result shows that the Channel MAC protocol outperforms the IEEE 802.11 in throughput due to its channel diversity mechanism in spite of the prediction errors and packet collisions. Next, we extend the Channel MAC protocol to support multi-rate communications. At present, two prominent multi-rate mechanisms, Opportunistic Auto Rate (OAR) and Receiver Based Auto Rate (RBAR) are unable to adapt to short term changes in channel conditions during transmission as well as to use optimum power and throughput during packet transmissions. On the other hand, using channel predictions, each source-destinations pair in Channel MAC can fully utilise the non-fade durations. We combine the scheduling of Channel MAC and the rate adaptive transmission based on the channel state information to design the 'Rate Adaptive Channel MAC' protocol. However, to implement the Rate adaptive Channel MAC, we need to use a channel prediction scheme to identify transmission opportunities as well as auto rate adaptation mechanism to select rates and number of packets to transmit during those times. For channel prediction, we apply the scheme proposed for the practical implementation of Channel MAC. We propose a "safety margin" based technique to provide auto rate adaptation. Simulation results show that a significant performance improvement can be achieved by Rate adaptive Channel MAC as compared to existing rate adaptive protocols such as OAR.

Opportunistic communication techniques have shown to provide significant performance improvements in centralised random access wireless networks. The key mechanism of opportunistic communication is to send back-to-back data packets whenever the channel quality is deemed "good". Recently there have been attempts to introduce opportunistic communication techniques in distributed wireless networks such as wireless ad hoc networks. In line of this research, we propose a new paradigm of medium access control, called Channel MAC based on the channel randomness and opportunistic communication principles. Scheduling in Channel MAC depends on the instance at which the channel quality improves beyond a threshold, while neighbouring nodes are deemed to be silent. Once a node starts transmitting, it will keep transmitting until the channel becomes "bad". We derive an analytical throughput equation of the proposed MAC in a multiple access environment and validate it by simulations. It is observed that Channel MAC outperforms IEEE 802.11 for all probabilities of good channel condition and all numbers of nodes. For higher number of nodes, Channel MAC achieves higher throughput at lower probabilities of good channel condition increasing the operating range. Furthermore, the total throughput of the network grows with increasing number of nodes considering negligible propagation delay in the network. A scalable channel prediction scheme is required to implement the practical Channel MAC protocol in practice. We propose a mean-value based channel prediction scheme, which provides prediction with enough accuracy to be used in the Channel MAC protocol. NS2 simulation result shows that the Channel MAC protocol outperforms the IEEE 802.11 in throughput due to its channel diversity mechanism in spite of the prediction errors and packet collisions. Next, we extend the Channel MAC protocol to support multi-rate communications. At present, two prominent multi-rate mechanisms, Opportunistic Auto Rate (OAR) and Receiver Based Auto Rate (RBAR) are unable to adapt to short term changes in channel conditions during transmission as well as to use optimum power and throughput during packet transmissions. On the other hand, using channel predictions, each source-destinations pair in Channel MAC can fully utilise the non-fade durations. We combine the scheduling of Channel MAC and the rate adaptive transmission based on the channel state information to design the 'Rate Adaptive Channel MAC' protocol. However, to implement the Rate adaptive Channel MAC, we need to use a channel prediction scheme to identify transmission opportunities as well as auto rate adaptation mechanism to select rates and number of packets to transmit during those times. For channel prediction, we apply the scheme proposed for the practical implementation of Channel MAC. We propose a "safety margin" based technique to provide auto rate adaptation. Simulation results show that a significant performance improvement can be achieved by Rate adaptive Channel MAC as compared to existing rate adaptive protocols such as OAR.