Ubuntu, Jesus, and Earth: Integrating African Religion and Christianity in Ecological Ethics

Africa faces problems of ecological devastation caused by economic exploitation, rapid population growth, and poverty. Capitalism, residual colonialism, and corruption undermine Africa's efforts to forge a better future. The dissertation describes how in Africa the mounting ecological crisis has religious, political, and economic roots that enable and promote social and environmental harm. It presents the thesis that religious traditions, including their ethical expressions, can effectively address the crisis, ameliorate its impacts, and advocate for social and environmental betterment, now and in the future. First, it examines African traditional religion and Christian teaching, which together provide the foundation for African Christianity. Critical examination of both religious worldviews uncovers their complementary emphases on human responsibility toward planet Earth and future generations. Second, an analysis of the Gwembe Tonga of Chief Simamba explores the interconnectedness of all elements of the universe in African cosmologies. In Africa, an interdependent, participatory relationship exists between the world of animals, the world of humans, and the Creator. In discussing the annual lwiindi (rain calling) ceremony of Simamba, the study explores ecological overtones of African religions. Such rituals illustrate the involvement of ancestors and high gods in maintaining ecological integrity. Third, the foundation of the African morality of abundant life is explored. Across Sub-Saharan Africa, ancestors' teachings are the foundation of morality; ancestors are guardians of the land. A complementary teaching that Christ is the ecological ancestor of all life can direct ethical responses to the ecological crisis. Fourth, the eco-social implications of ubuntu (what it means to be fully human) are examined. Some aspects of ubuntu are criticized in light of economic inequalities and corruption in Africa. However, ubuntu can be transformed to advocate for eco-social liberation. Fifth, the study recognizes that in some cases conflicts exist between ecological values and religious teachings. This conflict is examined in terms of the contrast between awareness of socioeconomic problems caused by population growth, on the one hand, and advocacy of a traditional African morality of abundant children, on the other hand. A change in the latter religious view is needed since overpopulation threatens sustainable living and the future of Earth. The dissertation concludes that the identification of Jesus with African ancestors and theological recognition of Jesus as the ecological ancestor, woven together with ubuntu, an ethic of interconnectedness, should characterize African consciousness and promote resolution of the socio-ecological crisis.

Using Markets and Spam to Combat Malware

We propose an economic mechanism to reduce the incidence of malware that delivers spam. Earlier research proposed attention markets as a solution for unwanted messages, and showed they could provide more net benefit than alternatives such as filtering and taxes. Because it uses a currency system, Attention Bonds faces a challenge. Zombies, botnets, and various forms of malware might steal valuable currency instead of stealing unused CPU cycles. We resolve this problem by taking advantage of the fact that the spam-bot problem has been reduced to financial fraud. As such, the large body of existing work in that realm can be brought to bear. By drawing an analogy between sending and spending, we show how a market mechanism can detect and prevent spam malware. We prove that by using a currency (i) each instance of spam increases the probability of detecting infections, and (ii) the value of eradicating infections can justify insuring users against fraud. This approach attacks spam at the source, a virtue missing from filters that attack spam at the destination. Additionally, the exchange of currency provides signals of interest that can improve the targeting of ads. ISPs benefit from data management services and consumers benefit from the higher average value of messages they receive. We explore these and other secondary effects of attention markets, and find them to offer, on the whole, attractive economic benefits for all – including consumers, advertisers, and the ISPs.

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.

Foreground Object Segmentation from Binocular Stereo Video

Moving cameras are needed for a wide range of applications in robotics, vehicle systems, surveillance, etc. However, many foreground object segmentation methods reported in the literature are unsuitable for such settings; these methods assume that the camera is fixed and the background changes slowly, and are inadequate for segmenting objects in video if there is significant motion of the camera or background. To address this shortcoming, a new method for segmenting foreground objects is proposed that utilizes binocular video. The method is demonstrated in the application of tracking and segmenting people in video who are approximately facing the binocular camera rig. Given a stereo image pair, the system first tries to find faces. Starting at each face, the region containing the person is grown by merging regions from an over-segmented color image. The disparity map is used to guide this merging process. The system has been implemented on a consumer-grade PC, and tested on video sequences of people indoors obtained from a moving camera rig. As can be expected, the proposed method works well in situations where other foreground-background segmentation methods typically fail. We believe that this superior performance is partly due to the use of object detection to guide region merging in disparity/color foreground segmentation, and partly due to the use of disparity information available with a binocular rig, in contrast with most previous methods that assumed monocular sequences.

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.