Link analysis algorithms to handle hanging and spam pages

Hanging pages are one of the hidden problems in link structure ranking algorithms and they can be compromised by spammers to induce Link Spam. In this thesis, efficient algorithms have been developed to produce fair and relevant results from Web search engines, by handling hanging pages and detecting Link spam caused by them. The effect of hanging pages in Website Optimisation is also investigated and methodologies were proposed to develop optimised websites

Training Datasets for Machine Reading Comprehension and Their Limitations

Neural networks are a powerful model class to learn machine Reading Comprehen- sion (RC), yet they crucially depend on the availability of suitable training datasets. In this thesis we describe methods for data collection, evaluate the performance of established models, and examine a number of model behaviours and dataset limita- tions. We first describe the creation of a data resource for the science exam QA do- main, and compare existing models on the resulting dataset. The collected ques- tions are plausible – non-experts can distinguish them from real exam questions with 55% accuracy – and using them as additional training data leads to improved model scores on real science exam questions. Second, we describe and apply a distant supervision dataset construction method for multi-hop RC across documents. We identify and mitigate several dataset assembly pitfalls – a lack of unanswerable candidates, label imbalance, and spurious correlations between documents and particular candidates – which often leave shallow predictive cues for the answer. Furthermore we demonstrate that se- lecting relevant document combinations is a critical performance bottleneck on the datasets created. We thus investigate Pseudo-Relevance Feedback, which leads to improvements compared to TF-IDF-based document combination selection both in retrieval metrics and answer accuracy. Third, we investigate model undersensitivity: model predictions do not change when given adversarially altered questions in SQUAD2.0 and NEWSQA, even though they should. We characterise affected samples, and show that the phe- nomenon is related to a lack of structurally similar but unanswerable samples during training: data augmentation reduces the adversarial error rate, e.g. from 51.7% to 20.7% for a BERT model on SQUAD2.0, and improves robustness also in other settings. Finally we explore efficient formal model verification via Interval Bound Propagation (IBP) to measure and address model undersensitivity, and show that using an IBP-derived auxiliary loss can improve verification rates, e.g. from 2.8% to 18.4% on the SNLI test set

A study of the experiential service design process at a luxury hotel

This thesis explores the process of designing experiential services at a luxury hotel. These processes were surfaced by means of a methodology that used the principles of jazz improvisation. Due to similarities between experiential service design and elements in jazz improvisation, representing experiential service design through the jazz improvisation metaphor leads to a new framework for exploring the process of experiential service design that is iterative in nature. A gap in the service design literature is that experiential service design is not operationalized in organizational improvisation, and one contribution from this study will be to fill that gap. This study contributes to the field of knowledge by exposing a new perspective on how experiential services can be better designed by adapting some of the design tools from this luxury hotel; a second contribution is a recommendation for how the improvisational lens works as an investigative tool to research experiential organizations. In the process, some new dimensions to understanding complexity are contributed. The research process utilized qualitative research methods. Frank Barrett (1998) identified seven characteristics of jazz improvisation which I have used as a heuristic device: 1) provocative competence (i.e., deliberately creating disruption); 2) embracing errors as learning sources; 3) minimal structures that allow for maximum flexibility; 4) distributed task (i.e., an ongoing give and take); 5) reliance on retrospective sensemaking (organizational members as bricoleurs, making use of whatever is at hand); 6) hanging out (connecting through communities of practice); and 7) alternating between soloing and supporting. This research is grounded in the body of literature regarding complexity, organizational improvisation, service design and experience design. The role of heterogeneous minimal structures that are fluid and optimize uncertainty is central to this investigation. Themes such as sensemaking and the role of story, meaning-making, organizational actors' use of tangible and intangible design skills, and embracing ambiguity in efforts to design experiential services are explored throughout the dissertation. The anticipatory nature of experiential service design is a principle outcome from the data that is incorporated into the new conceptual framework highlighting a "posture of service"

Assessing the Reliability of Deep Learning Applications

Deep Learning (DL) applications are widely deployed in diverse areas, such as image classification, natural language processing, and auto-driving systems. Although these applications achieve outstanding performance in terms of accuracy, developers have raised strong concerns about their reliability since the logic of DL applications is a black box for humans. Specifically, DL applications learn the logic during stochastic training and encode the logic in high-dimensional weights of DL models. Unlike source code in conventional software, such weights are infeasible for humans to directly interpret, examine, and validate. As a result, the defects in DL applications are not easy to be detected in software development stages and may cause catastrophic accidents in safety-critical missions. Therefore, it is critical to adequately test DL applications in terms of reliability before they are deployed. This thesis aims to propose automatic approaches to testing DL applications from the perspective of reliability. It consists of the following three studies. The first study proposes object-relevancy, a property that reliable DL-based image classifiers should comply with, i.e., the classification results should be made based on the features relevant to the target object in a given image, instead of irrelevant features such as the background. This study further proposes a metamorphic testing approach and two corresponding metamorphic relations to assess if this property is violated in image classifications. The evaluation shows that the proposed approach can effectively detect the unreliable inferences violating the object-relevancy property, with the average precision 64.1% and 96.4% for the two relations, respectively. The subsequent empirical study reveals that such unreliable inferences are prevalent in the real world and the existing training strategies cannot tame this issue effectively. The second study concentrates on the reliability issues induced by model compression of DL applications. Model compression can significantly reduce the sizes of Deep Neural Network (DNN) models, and thus facilitate the dissemination of sophisticated, sizable DNN models. However, the prediction results of compressed models may deviate from those of their original models, resulting in unreliable DL applications in deployment. To help developers thoroughly understand the impact of model compression, it is essential to test these models to find those deviated behaviors before dissemination. This study proposes DFLARE, a novel, search-based, black-box testing technique. The evaluation shows that DFLARE constantly outperforms the baseline in both efficacy and efficiency. More importantly, the triggering inputs found by DFLARE can be used to repair up to 48.48% deviated behaviors. The third study focuses on the reliability of DL-based vulnerability detection (DLVD) techniques. DLVD techniques are designed to detect the vulnerability in the source code. However, these techniques may only capture the syntactic patterns of vulnerable code while ignoring the semantic information in the source code. As a result, malicious users can easily fool such techniques by manipulating the syntactic patterns of vulnerable code, e.g., variable renaming. This study proposes a new methodology to evaluate the learning ability of DLVD techniques, i.e., whether a DLVD technique can capture the semantic information from vulnerable source code and leverage such information in detection. Specifically, this approach creates a special dataset in which the vulnerable functions and non-vulnerable ones have almost identical syntactic code patterns but different semantic meanings. If a detection approach cannot capture the semantic difference between the vulnerable functions and the non-vulnerable ones, this approach will have low performance on the constructed dataset. Our preliminary results show that two common detection approaches are ineffective in capturing the semantic information from source code

Middeck Active Control Experiment (MACE), phase A

A rationale to determine which structural experiments are sufficient to verify the design of structures employing Controlled Structures Technology was derived. A survey of proposed NASA missions was undertaken to identify candidate test articles for use in the Middeck Active Control Experiment (MACE). The survey revealed that potential test articles could be classified into one of three roles: development, demonstration, and qualification, depending on the maturity of the technology and the mission the structure must fulfill. A set of criteria was derived that allowed determination of which role a potential test article must fulfill. A review of the capabilities and limitations of the STS middeck was conducted. A reference design for the MACE test article was presented. Computing requirements for running typical closed-loop controllers was determined, and various computer configurations were studied. The various components required to manufacture the structure were identified. A management plan was established for the remainder of the program experiment development, flight and ground systems development, and integration to the carrier. Procedures for configuration control, fiscal control, and safety, reliabilty, and quality assurance were developed

Demystifying Theoretical Sampling in Grounded Theory Research

Theoretical sampling is a central tenet of classic grounded theory and is essential to the development and refinement of a theory that is ‘grounded’ in data. While many authors appear to share concurrent definitions of theoretical sampling, the ways in which the process is actually executed remain largely elusive and inconsistent. As such, employing and describing the theoretical sampling process can present a particular challenge to novice researchers embarking upon their first grounded theory study. This article has been written in response to the challenges faced by the first author whilst writing a grounded theory proposal. It is intended to clarify theoretical sampling for new grounded theory researchers, offering some insight into the practicalities of selecting and employing a theoretical sampling strategy. It demonstrates that the credibility of a theory cannot be dissociated from the process by which it has been generated and seeks to encourage and challenge researchers to approach theoretical sampling in a way that is apposite to the core principles of the classic grounded theory methodology

Knowing the Indigenous Leadership Journey: Indigenous People Need the Academic System as Much as the Academic System Needs Native People

This dissertation explores the research question, “How can we create the best learning environments for Indigenous students through good leadership at all levels?” A bridge between cultures provides learning opportunities toward academic success between Indigenous students, families, leaders, and communities. Through personal experience as a practitioner, professional, and education, my research examines and identifies results from personnel and students at five schools, tribal and public, their tribal communities, and two Indigenous people in high profile leadership positions indicating an educational philosophy recognizing Indigenous people need the academic system as much as the academic system needs Native people. Portraits and interviews revealed the existence of pedagogical methodologies oriented toward Native student success yet mainstream academic institutions are failing Native peoples to the detriment of their tribal communities. In many tribal communities, leadership beholds many styles, modeling modes of life amid Mother Earth, yet education needs to be bridged with philosophy. Through personal experiences and delving in to educational leadership, a life’s passion emerged toward Indigenous leadership philosophy to educate in collaborative and inclusive manners bridging perceptions between educators, Indigenous peoples, respective communities, and leadership building toward policy attainment. Academic opportunities for success with intergenerational Native students identify necessary interconnectedness with a leadership philosophy. Many successful leadership and education models compare to Indigenous styles from several hundred years ago. The literature reflects on challenges and academic success bridging cultural standards resulting in a range of academic and leadership interest among Native communities. The electronic version of this dissertation is at OhioLink ETD Center, http://www.ohiolink.edu/et