An Historical and Theological Analysis of Campus Crusade for Christ's Evangelistic Practice in Two American University Contexts

This dissertation, an exercise in practical theology, consists of a critical conversation between the evangelistic practice of Campus Crusade for Christ in two American university contexts, Bryan Stone's ecclesiologically grounded theology of evangelism, and William Abraham's eschatologically grounded theology of evangelism. It seeks to provide these evangelizing communities several strategic proposals for a more ecclesiologically and eschatologically grounded practice of evangelism within a university context. The current literature on evangelism is long on evangelistic strategy and activity, but short on theological analysis and reflection. This study focuses on concrete practices, but is grounded in a thick description of two particular contexts (derived from qualitative research methods) and a theological analysis of the ecclesiological and eschatological beliefs embedded within their evangelistic activities. The dissertation provides an historical overview of important figures, ideas, and events that helped mold the practice of evangelism inherited by the two ministries of this study, beginning with the famous Haystack Revival on Williams College in 1806. Both ministries, Campus Crusade for Christ at Bowling Green State University (Ohio) and at Washington State University, inherited an evangelistic practice sorely infected with many of the classic distortions that both Abraham and Stone attempt to correct. Qualitative research methods detail the direction that Campus Crusade for Christ at Bowling Green State University (Ohio) and Washington State University have taken the practice of evangelism they inherited. Applying the analytical categories that emerge from a detailed summary of Stone and Abraham to qualitative data of these two ministries reveals several ways evangelism has morphed in a manner sympathetic to Stone's insistence that the central logic of evangelism is the embodied witness of the church. The results of this analysis reveal the subversive and pervasive influence of modernity on these evangelizing communities—an influence that warrants several corrective strategic proposals including: 1) re-situating evangelism within a reading of the biblical narrative that emphasizes the present, social, public, and realized nature of the gospel of the kingdom of God rather than simply its future, personal, private, and unrealized dimensions; 2) clarifying the nature of the evangelizing communities and their relationship to the church; and 3) emphasizing the virtues that characterize a new evangelistic exemplar who is incarnational, intentional, humble, and courageous.

A Catalogue of the Alpha Chapter Boston University Graduates of the School of Theology 1850-1905

A listing of graduate of Boston University School of Theology and predecessor school. Arranged by class year, alphabetical by last name and geographically by region.

Library Technology Services at Boston University: an un-roadmap

A working paper for discussion

Safe Compositional Specification of Networking Systems

The Science of Network Service Composition has clearly emerged as one of the grand themes driving many of our research questions in the networking field today [NeXtworking 2003]. This driving force stems from the rise of sophisticated applications and new networking paradigms. By "service composition" we mean that the performance and correctness properties local to the various constituent components of a service can be readily composed into global (end-to-end) properties without re-analyzing any of the constituent components in isolation, or as part of the whole composite service. The set of laws that would govern such composition is what will constitute that new science of composition. The combined heterogeneity and dynamic open nature of network systems makes composition quite challenging, and thus programming network services has been largely inaccessible to the average user. We identify (and outline) a research agenda in which we aim to develop a specification language that is expressive enough to describe different components of a network service, and that will include type hierarchies inspired by type systems in general programming languages that enable the safe composition of software components. We envision this new science of composition to be built upon several theories (e.g., control theory, game theory, network calculus, percolation theory, economics, queuing theory). In essence, different theories may provide different languages by which certain properties of system components can be expressed and composed into larger systems. We then seek to lift these lower-level specifications to a higher level by abstracting away details that are irrelevant for safe composition at the higher level, thus making theories scalable and useful to the average user. In this paper we focus on services built upon an overlay management architecture, and we use control theory and QoS theory as example theories from which we lift up compositional specifications.

Learning a Family of Detectors

Object detection and recognition are important problems in computer vision. The challenges of these problems come from the presence of noise, background clutter, large within class variations of the object class and limited training data. In addition, the computational complexity in the recognition process is also a concern in practice. In this thesis, we propose one approach to handle the problem of detecting an object class that exhibits large within-class variations, and a second approach to speed up the classification processes. In the first approach, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved with using a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. For applications where explicit parameterization of the within-class states is unavailable, a nonparametric formulation of the kernel can be constructed with a proper foreground distance/similarity measure. Detector training is accomplished via standard Support Vector Machine learning. The resulting detectors are tuned to specific variations in the foreground class. They also serve to evaluate hypotheses of the foreground state. When the image masks for foreground objects are provided in training, the detectors can also produce object segmentation. Methods for generating a representative sample set of detectors are proposed that can enable efficient detection and tracking. In addition, because individual detectors verify hypotheses of foreground state, they can also be incorporated in a tracking-by-detection frame work to recover foreground state in image sequences. To run the detectors efficiently at the online stage, an input-sensitive speedup strategy is proposed to select the most relevant detectors quickly. The proposed approach is tested on data sets of human hands, vehicles and human faces. On all data sets, the proposed approach achieves improved detection accuracy over the best competing approaches. In the second part of the thesis, we formulate a filter-and-refine scheme to speed up recognition processes. The binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the face recognition grand challenge version 2 data set, hand shape detection and parameter estimation on a hand data set, and vehicle detection and estimation of the view angle on a multi-pose vehicle data set. On all data sets, our approach is at least five times faster than simply evaluating all foreground state hypotheses with virtually no loss in classification accuracy.

Face Identification by a Cascade of Rejection Classifiers

Nearest neighbor search is commonly employed in face recognition but it does not scale well to large dataset sizes. A strategy to combine rejection classifiers into a cascade for face identification is proposed in this paper. A rejection classifier for a pair of classes is defined to reject at least one of the classes with high confidence. These rejection classifiers are able to share discriminants in feature space and at the same time have high confidence in the rejection decision. In the face identification problem, it is possible that a pair of known individual faces are very dissimilar. It is very unlikely that both of them are close to an unknown face in the feature space. Hence, only one of them needs to be considered. Using a cascade structure of rejection classifiers, the scope of nearest neighbor search can be reduced significantly. Experiments on Face Recognition Grand Challenge (FRGC) version 1 data demonstrate that the proposed method achieves significant speed up and an accuracy comparable with the brute force Nearest Neighbor method. In addition, a graph cut based clustering technique is employed to demonstrate that the pairwise separability of these rejection classifiers is capable of semantic grouping.