Active learning is a practical field of machine learning as labeling data or determining which data to label can be a time consuming and inefficient task. Active learning automates the process of selecting which data to label, but current methods are heavily model reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing Combinatorial Coverage to overcome these issues. The proposed methods are data-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to different models and has a competitive sampling bias compared to benchmark methods. / Master of Science / Machine learning (ML) models are being used frequently in a variety of applications. For the model to be able to learn, data is required. Processing this data is often one of the most, if not the most, time consuming aspects of utilizing ML. One especially burdensome aspect of data processing is data labeling, or determining what each data point corresponds to in terms of real world class. For example, determining if a data point that is an image contains a plane or bird. This way the ML model can learn from the data. Active learning is a sub-field of machine learning which aims to ease this burden by allowing the model to select which data would be most beneficial to label, so that the entirety of the dataset does not need to be labeled. The issue with current active learning methods is that they are highly model dependent. In machine learning deployment the model being used may change while data stays the same, so this model dependency can cause for data points we label with respect to one model to not be ideal for another model. This model dependency has led to sampling bias issues as well; points which are chosen to be labeled may all be similar or not representative of all data resulting in the ML model not being as knowledgeable as possible. Relevant work has focused on the sampling bias issue, and several methods have been proposed to combat this issue. Few of the methods are applicable to any type of ML model though. The issue of sampled points not generalizing to different models has been studied but no solutions have been proposed. In this work we present active learning methods using Combinatorial Coverage. Combinatorial Coverage is a statistical technique from the field of Design of Experiments, and has commonly been used to design test sets. The extension of Combinatorial Coverage to ML is newer, and provides a way to focus on the data. We show that this data focused approach to active learning achieves a better performance when the sampled data is used for a different model and that it achieves a competitive sampling bias compared to benchmark methods.

The purpose of the present study was to further explore evidence for the construct-related validity of the Sulliman Scale of Social Interest (SSSI) through the implementation of both convergent and discriminant procedures. This was done through (a) replicating St. John's 1992 study, (b) extending the findings of that study by incorporating additional psychological measures, and (c) examining SSI itself by means of principal axis factor analytic procedures. First, all nine of the relationships demonstrated between the SSSI and other variables in the St. John (1992) study were replicated in the present study. Second, in extending the findings of that study, 22 of 26 hypothesized relationships between the SSSI and other psychological measures were in the predicted direction. Third, the results of the factor analysis produced three factors labeled "contextual harmony," "positive treatment/response," and "confidence and trust." Taken together, the outcomes of both studies appear to offer some support for the SSI's construct validity and to provide possible directions for future research.

abstract: The construction of many families of combinatorial objects remains a challenging problem. A t-restriction is an array where a predicate is satisfied for every t columns; an example is a perfect hash family (PHF). The composition of a PHF and any t-restriction satisfying predicate P yields another t-restriction also satisfying P with more columns than the original t-restriction had. This thesis concerns three approaches in determining the smallest size of PHFs. Firstly, hash families in which the associated property is satisfied at least some number lambda times are considered, called higher-index, which guarantees redundancy when constructing t-restrictions. Some direct and optimal constructions of hash families of higher index are given. A new recursive construction is established that generalizes previous results and generates higher-index PHFs with more columns. Probabilistic methods are employed to obtain an upper bound on the optimal size of higher-index PHFs when the number of columns is large. A new deterministic algorithm is developed that generates such PHFs meeting this bound, and computational results are reported. Secondly, a restriction on the structure of PHFs is introduced, called fractal, a method from Blackburn. His method is extended in several ways; from homogeneous hash families (every row has the same number of symbols) to heterogeneous ones; and to distributing hash families, a relaxation of the predicate for PHFs. Recursive constructions with fractal hash families as ingredients are given, and improve upon on the best-known sizes of many PHFs. Thirdly, a method of Colbourn and Lanus is extended in which they horizontally copied a given hash family and greedily applied transformations to each copy. Transformations of existential t-restrictions are introduced, which allow for the method to be applicable to any t-restriction having structure like those of hash families. A genetic algorithm is employed for finding the "best" such transformations. Computational results of the GA are reported using PHFs, as the number of transformations permitted is large compared to the number of symbols. Finally, an analysis is given of what trade-offs exist between computation time and the number of t-sets left not satisfying the predicate. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

abstract: Modern software and hardware systems are composed of a large number of components. Often different components of a system interact with each other in unforeseen and undesired ways to cause failures. Covering arrays are a useful mathematical tool for testing all possible t-way interactions among the components of a system. The two major issues concerning covering arrays are explicit construction of a covering array, and exact or approximate determination of the covering array number---the minimum size of a covering array. Although these problems have been investigated extensively for the last couple of decades, in this thesis we present significant improvements on both of these questions using tools from the probabilistic method and randomized algorithms. First, a series of improvements is developed on the previously known upper bounds on covering array numbers. An estimate for the discrete Stein-Lovász-Johnson bound is derived and the Stein- Lovász -Johnson bound is improved upon using an alteration strategy. Then group actions on the set of symbols are explored to establish two asymptotic upper bounds on covering array numbers that are tighter than any of the presently known bounds. Second, an algorithmic paradigm, called the two-stage framework, is introduced for covering array construction. A number of concrete algorithms from this framework are analyzed, and it is shown that they outperform current methods in the range of parameter values that are of practical relevance. In some cases, a reduction in the number of tests by more than 50% is achieved. Third, the Lovász local lemma is applied on covering perfect hash families to obtain an upper bound on covering array numbers that is tightest of all known bounds. This bound leads to a Moser-Tardos type algorithm that employs linear algebraic computation over finite fields to construct covering arrays. In some cases, this algorithm outperforms currently used methods by more than an 80% margin. Finally, partial covering arrays are introduced to investigate a few practically relevant relaxations of the covering requirement. Using probabilistic methods, bounds are obtained on partial covering arrays that are significantly smaller than for covering arrays. Also, randomized algorithms are provided that construct such arrays in expected polynomial time. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Schubert K., Polte T., Bönisch U., Schader S., Holtappels R., Hildebrandt G., Lehmann J., Simon J.C., Anderegg U., Saalbach A. Thy-1 (CD90) regulates the extravasation of leukocytes during inflammation. Eur. J. Immunol. 41, 2011; 645-656 / Saalbach A, Haustein UF, and Anderegg U. A ligand of human Thy-1 is localized on polymorphonuclear leukocytes and monocytes and mediates the binding to activated Thy-1 positive microvascular endothelial cells and fibroblasts. J. Invest. Dermatol. 2000; 115: 882-888 / Wetzel A., Wetzig T., Haustein U.F., Sticherling M., Anderegg U., Simon J.C., Saalbach A. Increased Neutrophil Adherence in Psoriasis: Role of the Human Endothelial Cell Receptor Thy-1 (CD90). Journal of Investigative Dermatology. 2006; 126: 441–452 / Saalbach A., Arnhold J., Leßig J., Simon J. C., Anderegg U. Human Thy-1 induces secretion of matrixmetalloproteinase-9 and CXCL8 from human neutrophils. Eur. J. Immunol. 2008; 38: 1391–1403 / Schmidt M., Gutknecht D., Simon J.C., Schulz J.N., Eckes B., Anderegg U., Saalbach A. Controlling the Balance of Fibroblast Proliferation and Differentiation: Impact of Thy-1. J Invest Dermatol. 2015; 135(7):1893-902 / Saalbach, A., Wetzig, T., Haustein, U.F., and Anderegg, U. Detection of human soluble Thy-1 in serum by ELISA: fibroblasts and activated endothelial cells are a possible source of soluble Thy-1 in serum. Cell Tiss. Res. 1999; 298: 307-315 / Wetzel A, Chavakis T, Preissner K, Sticherling M, Haustein U-F, Anderegg U, Saalbach A. Human Thy-1 (CD90) on Activated Endothel Cells is a Counterreceptor for the Leucocyte Integrin Mac-1 (CD11b/CD18). The Journal of Immunology 2004; 172: 3850-3859