Matching Items (12)

Scuttlebutt and Whuffie: Reputation in Distributed Networks

Description

Secure Scuttlebutt is a digital social network in which the network data is distributed among the users.<br/>This is done to secure several benefits, like offline browsing, censorship resistance, and to

Secure Scuttlebutt is a digital social network in which the network data is distributed among the users.<br/>This is done to secure several benefits, like offline browsing, censorship resistance, and to imitate natural social networks, but it comes with downsides, like the lack of an obvious implementation of a recommendation algorithm.<br/>This paper proposes Whuffie, an algorithm that tracks each user's reputation for having information that is interesting to a user using conditional probabilities.<br/>Some errors in the main Secure Scuttlebutt network prevent current large-scale testing of the usefulness of the algorithm, but testing on my own personal account led me to believe it a success.

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  • 2021-05

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Mathematical Modeling of Neuron and Network Dynamics

Description

The Morris-Lecar two-dimensional conductance-based model for an excitable membrane can be used to simulate neurons, and these neuron models can be connected to model neuronal networks. In this work, we

The Morris-Lecar two-dimensional conductance-based model for an excitable membrane can be used to simulate neurons, and these neuron models can be connected to model neuronal networks. In this work, we analyze the dynamics of the Morris-Lecar model using phase plane analysis, and we simulate the model with different parameter regimes. We also develop and simulate a two-cell model network, as well as larger networks composed of 17 cells. We show that the bifurcation type and the parameters for the synaptic connections between model neurons affect the model network dynamic behavior. In particular, we look at the synchronization of networks of identical, repetitively firing neurons.

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  • 2019-12

Women in TV Journalism

Description

In this research paper I combine statistics from various reports and studies with around 20 different interviews with female journalists to understand how women are faring in national and local

In this research paper I combine statistics from various reports and studies with around 20 different interviews with female journalists to understand how women are faring in national and local television newsrooms in 2019. I explore issues such as the pay gap, sexual assault, the importance of appearance, balancing work and family life and obstacles that women of color uniquely face. I spoke with women from various cultural backgrounds, experience levels, and in different positions within their newsrooms. Through my scholarly research and 19 interviews with women who either currently work at NBC News in New York City and women who currently or recently worked at 12News, the NBC affiliate in Phoenix, I conclude they share similar stories of oppression, sexism and issues. However, women have made more progress in local markets and have more opportunities when compared to the national level. I also explore reasons for why this disparity is happening and why local newsrooms seem to have more women represented through their on-air talent than national newsrooms do. One of the reasons I concluded for this include, how local newsrooms have a better understanding of their audience members thus making them more able to reflect their talent to their diverse audience. Another factor that might play a role in this disparity includes, the historical factor and societal norm of seeing men in higher positions and authoritative roles, such as being an anchor, at the network level. Lastly, the idea of how family and having children impacts women’s careers more than men. This can lead to less women pursuing a job at the network since they must spend time raising a family and have the ability and flexibility to do that easier at the local level. Overall, I focused on the barriers, obstacles and stories these women have had throughout their careers all while looking at it from both a local perspective and a national one.

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Created

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  • 2019-05

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Mathematical Modeling of the YAP/TAZ Pathways

Description

YAP/TAZ is the key effector in the Hippo pathway, but it is also involved in many other regulatory pathways to control tissue and organ size. To better understand its regulation

YAP/TAZ is the key effector in the Hippo pathway, but it is also involved in many other regulatory pathways to control tissue and organ size. To better understand its regulation and effects in tumorigenesis and degeneration, a preliminary feedback network was created with the species YAP/TAZ, phosphorylated YAP/TAZ, LATS, miR-130a, VGLL4, and β-catenin. From this network a set of ordinary differential equations were written and analyzed for parameter effects. A model showing the healthy, tumorigenic, and degenerative states was created and preliminary parameter analysis identified the effects of parameter modifications on the overall levels of YAP/TAZ. Further analysis is required and connections with the underlying biology should continue to be pursued to better understand how parameter modifications could improve disease treatments.

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  • 2019-05

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Soil moisture availability and energetic controls on belowground network complexity and function in arid ecosystems

Description

The explicit role of soil organisms in shaping soil health, rates of pedogenesis, and resistance to erosion has only just recently begun to be explored in the last century. However,

The explicit role of soil organisms in shaping soil health, rates of pedogenesis, and resistance to erosion has only just recently begun to be explored in the last century. However, much of the research regarding soil biota and soil processes is centered on maintaining soil fertility (e.g., plant nutrient availability) and soil structure in mesic- and agro- ecosystems. Despite the empirical and theoretical strides made in soil ecology over the last few decades, questions regarding ecosystem function and soil processes remain, especially for arid areas. Arid areas have unique ecosystem biogeochemistry, decomposition processes, and soil microbial responses to moisture inputs that deviate from predictions derived using data generated in more mesic systems. For example, current paradigm predicts that soil microbes will respond positively to increasing moisture inputs in a water-limited environment, yet data collected in arid regions are not congruent with this hypothesis. The influence of abiotic factors on litter decomposition rates (e.g., photodegradation), litter quality and availability, soil moisture pulse size, and resulting feedbacks on detrital food web structure must be explicitly considered for advancing our understanding of arid land ecology. However, empirical data coupling arid belowground food webs and ecosystem processes are lacking. My dissertation explores the resource controls (soil organic matter and soil moisture) on food web network structure, size, and presence/absence of expected belowground trophic groups across a variety of sites in Arizona.

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Created

Date Created
  • 2014

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A computational framework for quality of service measurement, visualization and prediction in mission critical communication networks

Description

Network traffic analysis by means of Quality of Service (QoS) is a popular research and development area among researchers for a long time. It is becoming even more relevant recently

Network traffic analysis by means of Quality of Service (QoS) is a popular research and development area among researchers for a long time. It is becoming even more relevant recently due to ever increasing use of the Internet and other public and private communication networks. Fast and precise QoS analysis is a vital task in mission-critical communication networks (MCCNs), where providing a certain level of QoS is essential for national security, safety or economic vitality. In this thesis, the details of all aspects of a comprehensive computational framework for QoS analysis in MCCNs are provided. There are three main QoS analysis tasks in MCCNs; QoS measurement, QoS visualization and QoS prediction. Definitions of these tasks are provided and for each of those, complete solutions are suggested either by referring to an existing work or providing novel methods.

A scalable and accurate passive one-way QoS measurement algorithm is proposed. It is shown that accurate QoS measurements are possible using network flow data.

Requirements of a good QoS visualization platform are listed. Implementations of the capabilities of a complete visualization platform are presented.

Steps of QoS prediction task in MCCNs are defined. The details of feature selection, class balancing through sampling and assessing classification algorithms for this task are outlined. Moreover, a novel tree based logistic regression method for knowledge discovery is introduced. Developed prediction framework is capable of making very accurate packet level QoS predictions and giving valuable insights to network administrators.

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Created

Date Created
  • 2014

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Formation, measurement, and imputation of social ties

Description

Network analysis is a key conceptual orientation and analytical tool in the social sciences that emphasizes the embeddedness of individual behavior within a larger web of social relations. The network

Network analysis is a key conceptual orientation and analytical tool in the social sciences that emphasizes the embeddedness of individual behavior within a larger web of social relations. The network approach is used to better understand the cause and consequence of social interactions which cannot be treated as independent. The relational nature of network data and models, however, amplify the methodological concerns associated with inaccurate or missing data. This dissertation addresses such concerns via three projects. As a motivating substantive example, Project 1 examines factors associated with the selection of interaction partners by students at a large urban high school implementing a reform which, like many organizational improvement initiatives, is associated with a theory of change that posits changes to the structuring of social interactions as a central causal pathway to improved outcomes. A distinctive aspect of the data used in Project 1 is that it was a complete egocentric network census – in addition to being asked about their own relationships, students were asked about the relationships between alters that they nominated in the self-report. This enables two unique examinations of methodological challenges in network survey data collection: Project 2 examines the factors related to how well survey respondents assess the strength of social connections between others, finding that "informant" competence corresponds positively with their social proximity to target dyad as well as their centrality in the network. Project 3 explores using such third-party reports to augment network imputation methods, and finds that incorporating third-party reports into model-based methods provides a significant boost in imputation accuracy. Together these findings provide important implications for collecting and extrapolating data in research contexts where a complete social network census is highly desirable but infeasible.

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Created

Date Created
  • 2019

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Performance optimization of linux networking for latency-sensitive virtual systems

Description

Virtual machines and containers have steadily improved their performance over time as a result of innovations in their architecture and software ecosystems. Network functions and workloads are increasingly migrating

Virtual machines and containers have steadily improved their performance over time as a result of innovations in their architecture and software ecosystems. Network functions and workloads are increasingly migrating to virtual environments, supported by developments in software defined networking (SDN) and network function virtualization (NFV). Previous performance analyses of virtual systems in this context often ignore significant performance gains that can be acheived with practical modifications to hypervisor and host systems. In this thesis, the network performance of containers and virtual machines are measured with standard network performance tools. The performance of these systems utilizing a standard 3.18.20 Linux kernel is compared to that of a realtime-tuned variant of the same kernel. This thesis motivates improving determinism in virtual systems with modifications to host and guest kernels and thoughtful process isolation. With the system modifications described, the median TCP bandwidth of KVM virtual machines over bridged network interfaces, is increased by 10.8% with a corresponding reduction in standard deviation of 87.6%. Docker containers see a 8.8% improvement in median bandwidth and 4.4% reduction in standard deviation of TCP measurements using similar bridged networking. System tuning also reduces the standard deviation of TCP request/response latency (TCP RR) over bridged interfaces by 86.8% for virtual machines and 97.9% for containers. Hardware devices assigned to virtual systems also see reductions in variance, although not as noteworthy.

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Created

Date Created
  • 2015

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Visual Analytics Methods for Exploring Geographically Networked Phenomena

Description

The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal

The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models.

Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.

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Date Created
  • 2017

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Holistic learning for multi-target and network monitoring problems

Description

Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic

Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making.

The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm created in this dissertation. Specifically, a novel tree based ensemble that leverages the relationships between target attributes towards constructing a diverse, yet strong, model is proposed. The method is justified through its connection to existing methods and experimental evaluations on synthetic and real data.

The second topic pertains to monitoring complex systems that are modeled as networks. Such systems present a rich set of attributes and relationships for which holistic learning is important. In social networks, for example, in addition to friendship ties, various attributes concerning the users' gender, age, topic of messages, time of messages, etc. are collected. A restricted form of monitoring fails to take the relationships of multiple attributes into account, whereas the holistic view embeds such relationships in the monitoring methods. The focus is on the difficult task to detect a change that might only impact a small subset of the network and only occur in a sub-region of the high-dimensional space of the network attributes. One contribution is a monitoring algorithm based on a network statistical model. Another contribution is a transactional model that transforms the task into an expedient structure for machine learning, along with a generalizable algorithm to monitor the attributed network. A learning step in this algorithm adapts to changes that may only be local to sub-regions (with a broader potential for other learning tasks). Diagnostic tools to interpret the change are provided. This robust, generalizable, holistic monitoring method is elaborated on synthetic and real networks.

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Created

Date Created
  • 2014