In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented as grids or sequences. Due to this limitation, many existing deep learning approaches do not generalize to problem domains where the data is represented as graphs – social networks in social network analysis or molecular graphs in chemoinformatics, for instance. The goal of this thesis is to help bridge the gap by studying deep learning solutions that can handle graph data naturally. In particular, we explore deep learning-based approaches in the following areas. 1. Graph Attention. In the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to use an attention-based deep learning model. An attention mechanism allows the model to focus on task-relevant parts of the graph which helps the model make better decisions. We introduce a model for graph classification which uses an attention-guided walk to bias exploration towards more task-relevant parts of the graph. For the task of node classification, we study a different model – one with an attention mechanism which allows each node to select the most task-relevant neighborhood to integrate information from. 2. Graph Representation Learning. Graph representation learning seeks to learn a mapping that embeds nodes, and even entire graphs, as points in a low-dimensional continuous space. The function is optimized such that the geometric distance between objects in the embedding space reflect some sort of similarity based on the structure of the original graph(s). We study the problem of learning time-respecting embeddings for nodes in a dynamic network. 3. Brain Network Discovery. One of the fundamental tasks in functional brain analysis is the task of brain network discovery. The brain is a complex structure which is made up of various brain regions, many of which interact with each other. The objective of brain network discovery is two-fold. First, we wish to partition voxels – from a functional Magnetic Resonance Imaging scan – into functionally and spatially cohesive regions (i.e., nodes). Second, we want to identify the relationships (i.e., edges) between the discovered regions. We introduce a deep learning model which learns to construct a group-cohesive partition of voxels from the scans of multiple individuals in the same group. We then introduce a second model which can recover a hierarchical set of brain regions, allowing us to examine the functional organization of the brain at different levels of granularity. Finally, we propose a model for the problem of unified and group-contrasting edge discovery which aims to discover discriminative brain networks that can help us to better distinguish between samples from different classes.

Missing from extant conflict literature is an examination of both symmetric and asymmetric conflict ties. To address this void, network analysis was utilized to examine the responses (both symmetric and asymmetric conflict ties) of 140 employees and managers in four divisions of a large agency of the Federal Government. The study was limited to conflict over scarce resources. Conflict management methods were examined as well as the perceptions of how respondents both cope with and feel about conflict. The results indicate that when two people in a conflict setting are structurally equivalent they both report actions and feelings that are opposite from those of- the other person. This finding, an inverse contagion effect, has been termed diffusion resistance.

abstract: Despite the prevalence of teams in complex sociotechnical systems, current approaches to understanding workload tend to focus on the individual operator. However, research suggests that team workload has emergent properties and is not necessarily equivalent to the aggregate of individual workload. Assessment of communications provides a means of examining aspects of team workload in highly interdependent teams. This thesis set out to explore how communications are associated with team workload and performance under high task demand in all-human and human–autonomy teams in a command and control task. A social network analysis approach was used to analyze the communications of 30 different teams, each with three members operating in a command and control task environment of over a series of five missions. Teams were assigned to conditions differentiated by their composition with either a naïve participant, a trained confederate, or a synthetic agent in the pilot role. Social network analysis measures of centralization and intensity were used to assess differences in communications between team types and under different levels of demand, and relationships between communication measures, performance, and workload distributions were also examined. Results indicated that indegree centralization was greater in the all-human control teams than in the other team types, but degree centrality standard deviation and intensity were greatest in teams with a highly trained experimenter pilot. In all three team types, the intensity of communications and degree centrality standard deviation appeared to decrease during the high demand mission, but indegree and outdegree centralization did not. Higher communication intensity was associated with more efficient target processing and more successful target photos per mission, but a clear relationship between measures of performance and decentralization of communications was not found. / Dissertation/Thesis / Masters Thesis Human Systems Engineering 2020

Survey instruments utilized to quantify relationships, or aspects of relationships, may introduce multiple sources of nonindependence"”clustered variance"”into scores, including from actor, alter and dyadic sources. Estimating the magnitude of actor, alter and dyad nonindependence and their impact on the reliability of scores is an important step towards assuring quality data. Multilevel confirmatory factor analysis and the social relations model offer methods for quantifying the influence and estimating the reliability of multiple sources of clustered variance. The use of these methods is illustrated in the analysis of data gathered via a survey designed to quantify relational embeddedness in social network analyses.

Colder, harsher parenting attitudes and behaviours negatively impact children's behaviour and development, and have been linked to heightened levels of violence towards children. Parenting interventions can improve outcomes by reducing violent and increasing non-violent parenting behaviours. I investigated how changes associated with a low-cost positive parenting intervention spread through a rural, low-income, South African community. Specifically, I assessed whether exposure to a community-wide social activation process and Parenting for Lifelong Health (PLH) programmes (focused on violence prevention in low-resource settings) significantly predict: (1) improved parenting, and (2) change in the communication networks of female caregivers in the whole community, while controlling for variables such as psychiatric symptoms, parenting stress, and alcohol misuse. Additionally, I investigated whether ties to parenting programme attendees in the communication network predicted improved parenting. Afrikaans-speaking female caregivers (n = 235; mean age 35.92 years), with children aged between 1½ and 18 years old, participated in the intervention; three waves of data were collected (January 2016, June 2017, and February 2019). The social network was measured based on a peer nomination procedure (of study participants whom “you talk to about parenting”). To analyse the role of interpersonal ties as pathways for spreading intervention effects, I make use of Social Network Analysis (SNA), in the form of nominations of people with whom respondents discuss parenting, together with self-report measures of parenting-related outcomes (from caregivers and their children). I then trace the extent to which both the social activation process and the parenting programmes are effective, in part, via their diffusion throughout the community. SNA was used to disentangle whether network changes improved parenting practices (i.e., selection effects) or whether reported improvements in parenting practices improved caregiver information networks (i.e., socialisation effects). Analysis of data from waves 1 and 2 indicated that community-wide improvements in parenting behaviour were evidenced. The significant predictors of improvement were social activation “dose” received, change in network centrality and the influence of indirect exposure to the parenting programmes via attendees. Furthermore, attending at least one session of a parenting programme offered in the intervention significantly predicted change in the caregivers' communication networks, indicating the spread of social influence through their network. The small subset of caregivers (n = 51; 21.7%) attending one or more sessions of a parenting programme evidenced greater activity (i.e., covariate ego effect) and potential influence (i.e., covariate alter effect) within the communication network compared to caregivers who did not attend any programme sessions. This subset of attending caregivers were more likely to reach out to other caregivers to speak about parenting after being exposed to the intervention, and both sought and received social support from other caregivers. Follow-up assessment using a third wave of data showed that while attendees remained socially influential within the caregiver network the overall community improvement was not sustained. These results illustrate the value of social network analysis for ascertaining the pathways through which the intervention achieved its impact and tracking the evolution of social norms within a community. The results indicate an association between spill-over effects from attendees to non-attendees and community-wide changes through targeted interventions.

My dissertation focuses on the strategic implications of the link between organizational culture and social network structure. I study their role in the process of knowledge transfer and diffusion, organizational memory, and organizational design. More broadly, I examine the way that social structure influences the information environment, and what effect this has on organizational learning. I focus in particular on the process of cultural evolution. My dissertation leverages digitization as a phenomenon of inherent interest and as an empirical setting that can improve our theoretical understanding of both digital and non-digital communities. I have developed an expertise in computational methods, especially in machine learning techniques related to text and other unstructured data, and in the analysis of "big data," especially pertaining to large-scale networks. By combining these computational tools with organizational theory and the rich relational data generated by the explosion of digital records, my research grants insight into the dynamic process of learning in organizations and the implications for innovation and competitive advantage. I explore how digitization informs and develops our understanding of organizational culture, knowledge transfer, and the labor market. Specifically, I investigate how digitization has opened a window to observe network structure and language, providing a lasting record of these changes through time. Using these digital records to observe the structure of social relations and the language used to communicate can help deepen our theory of knowledge transfer for a wide range of organizations, not just those that operate in the digital sphere. This means that these studies also have implications for understanding organizations in non-digital settings. My dissertation contributes both theoretically and empirically to the knowledge theory of the firm. However, the mechanisms underlying knowledge transfer remain underdeveloped. I contribute by disentangling the related mechanisms of language and organizational structure, and I propose that common language directly impacts what knowledge may be efficiently transferred. Next, my dissertation contributes to the growing field of digitization. Digitization is salient for researchers both as a unique phenomenon and as an ever-expanding source of accessible data to test theory. Moreover, since one of the central contributions of digitization is to reduce the cost of information gathering, it is well-suited to my theoretical setting of knowledge transmission and organizational memory. Finally, my dissertation contributes to our understanding of culture in organizations. The focus on language as an aspect of culture allows both additional formalization as well as more specific empirical tests of the contribution of culture to organizational outcomes. In particular, a focus on dynamic settings in each of the chapters reveals the interplay between organizational structure, memory, and change. This helps us to understand how language evolves, how it is learned, and how it changes in response to information shocks.

The solutions presented in this dissertation describe a new paradigm in which we shepherd these network security protocols through atmosphere transitions, offering new ways to analyze and monitor the state of the protocol. The approach involves identifying a protocols transitional weaknesses through adaption of formal models, measuring the weakness as it exists in the wild by statically analyzing applications, and show how to use network traffic analysis to monitor protocol implementations going into the future. Throughout the effort, we follow the popular Open Authorization protocol in its attempts to apply its web-based roots to a mobile atmosphere. To pinpoint protocol deficiencies, we first adapt a well regarded formal analysis and show it insufficient in the characterization of mobile applications, tying its transitional weaknesses to implementation issues and delivering a reanalysis of the proof. We then measure the prevalence of this weakness by statically analyzing over 11,000 Android applications. While looking through source code, we develop new methods to find sensitive protocol information, overcome hurdles like obfuscation, and provide interfaces for later modeling, all while achieving a false positive rate of below 10 percent. We then use network analysis to detect and verify application implementations. By collecting network traffic from Android applications that use OAuth, we produce a set of metrics that when fed into machine learning classifiers, can identify if the OAuth implementation is correct. The challenges include encrypted network communication, heterogeneous device types, and the labeling of training data.

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The aim of this master’s thesis is to build stand-alone impedance analyzer for mesasuring antennas’ impedance. The vector network analysis is used as measuring method. Analog (with directional coupler and gain and phase detector) and digital (with 8bit AVR microcontrollers) part of analyzer are described and then build.

This diploma thesis deals with creating a project proposal of the research center construction according to the required specifications by SEDOX Company Ltd. The theoretical part describes the basic knowledge and tools of a project management – analytical tools for evaluating the financial and time project costs, also risk analysis relating to competitiveness and maintaining on top of the market. The third part is devoted to the current situation of the development application market. There is the complete proposal of the research centre construction, using the tools according to the IPMA (International Project Management Association) that were described in the theoretical part. In conclusion I have presented my own solution suggestions, their benefits, project sustainability, return of the investment cost and also long-term prosperity.