<p>In today’s ever changing business landscape, technology and innovation projects play a key role in creating competitive advantages for an organisation. However, many such projects are often hampered by under performance, cost overruns and lower than predicted revenue (Morris and Hough, 1987 and Christoffersen et al, 1992). This seems to indicate the lack of risk management in the way we manage projects. On the other hand, it is impossible to have any projects without risks. Thus, it is essential to have effective risk management rather than trying to eliminate risk out of projects. These factors have guided this study to focus on understanding the way risk assessment is performed in international technology projects. It aims to identify the link between risk assessment and project categorization, drawing from the ransaction cost economics (TCE) perspective. A qualitative approach applying semi-structured interviews was conducted with ten interviewees holding different roles in the engineering and technology projects within a multinational company with presence in more than 100 countries around the world. The application of the data display and analysis technique by Miles and Huberman (1984, 1994) enables initial findings to be presented using the “dendogram” method, thereafter, leading to the development of a two-dimensional risk assessment matrix as the final result of this study. </p>

If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

In a recent experiment, Freedman et al. recorded from inferotemporal (IT) and prefrontal cortices (PFC) of monkeys performing a "cat/dog" categorization task (Freedman 2001 and Freedman, Riesenhuber, Poggio, Miller 2001). In this paper we analyze the tuning properties of view-tuned units in our HMAX model of object recognition in cortex (Riesenhuber 1999) using the same paradigm and stimuli as in the experiment. We then compare the simulation results to the monkey inferotemporal neuron population data. We find that view-tuned model IT units that were trained without any explicit category information can show category-related tuning as observed in the experiment. This suggests that the tuning properties of experimental IT neurons might primarily be shaped by bottom-up stimulus-space statistics, with little influence of top-down task-specific information. The population of experimental PFC neurons, on the other hand, shows tuning properties that cannot be explained just by stimulus tuning. These analyses are compatible with a model of object recognition in cortex (Riesenhuber 2000) in which a population of shape-tuned neurons provides a general basis for neurons tuned to different recognition tasks.

Induction of classifiers from sets of preclassified training examples is one of the most popular machine learning tasks. This dissertation focuses on the techniques needed in the field of automated text categorization. Here, each document can be labeled with more than one class, sometimes with many classes. Moreover, the classes are hierarchically organized, the mutual relations being typically expressed in terms of a generalization tree. Both aspects (multi-label classification and hierarchically organized classes) have so far received inadequate attention. Existing literature work largely assumes that it is enough to induce a separate binary classifier for each class, and the question of class hierarchy is rarely addressed. This, however, ignores some serious problems. For one thing, induction of thousands of classifiers from hundreds of thousands of examples described by tens of thousands of features (a common case in automated text categorization) incurs prohibitive computational costs---even a single binary classifier in domains of this kind often takes hours, even days, to induce. For another, the circumstance that the classes are hierarchically organized affects the way we view the classification performance of the induced classifiers. The presented work proposes a technique referred to by the acronym "H-kNN-plus." The technique combines support vector machines and nearest neighbor classifiers with the intention to capitalize on the strengths of both. As for performance evaluation, a variety of measures have been used to evaluate hierarchical classifiers, including the standard non-hierarchical criteria that assign the same weight to different types of error. The author proposes a performance measure that overcomes some of their weaknesses. The dissertation begins with a study of (non-hierarchical) multi-label classification. One of the reasons for the poor performance of earlier techniques is the class-imbalance problem---a small number of positive examples being outnumbered by a great many negative examples. Another difficulty is that each of the classes tends to be characterized by a different set of characteristic features. This means that most of the binary classifiers are induced from examples described by predominantly irrelevant features. Addressing these weaknesses by majority-class undersampling and feature selection, the proposed technique significantly improves the overall classification performance. Even more challenging is the issue of hierarchical classification. Here, the dissertation introduces a new induction mechanism, H-kNN-plus, and subjects it to extensive experiments with two real-world datasets. The results indicate its superiority, in these domains, over earlier work in terms of prediction performance as well as computational costs.

The present study proposes a significant role for host country nationals (HCNs) in the expatriate adjustment process. Based on self-categorizaton theory, newcomer socialization research, organizational citizenship behavior (OCB) research, and models of expatriate adjustment, I present a model proposing how social categorization processes influence HCNs' willingness to engage in adjustment-facilitating organizational citizenship behaviors (AOCBs). I further propose that these behaviors have a significant impact on expatriates' adjustment and in turn, other important job-related outcomes of the expatriate. Hypotheses were tested on 115 expatriates and 53 HCNs. Expatriates were contacted directly or via an organizational contact. HCNs were either contacted directly or nominated by their expatriate counterpart to participate in the study. Results reveal support for the main tenets of the model. The willingness to engage in AOCBs was related to outgroup categorization, collectivism, and perceptions of justice. Social support provided by HCNs was found to significantly relate to HCNs' perceptions of their expatriate co-worker's adjustment. Expatriates, however, indicated that spousal adjustment and language ability were more important for their own adjustment. Adjustment was related to other key expatriate outcomes. The research and managerial implications of these results are discussed.

In today’s ever changing business landscape, technology and innovation projects play a key role in creating competitive advantages for an organisation. However, many such projects are often hampered by under performance, cost overruns and lower than predicted revenue (Morris and Hough, 1987 and Christoffersen et al, 1992). This seems to indicate the lack of risk management in the way we manage projects. On the other hand, it is impossible to have any projects without risks. Thus, it is essential to have effective risk management rather than trying to eliminate risk out of projects. These factors have guided this study to focus on understanding the way risk assessment is performed in international technology projects. It aims to identify the link between risk assessment and project categorization, drawing from the ransaction cost economics (TCE) perspective. A qualitative approach applying semi-structured interviews was conducted with ten interviewees holding different roles in the engineering and technology projects within a multinational company with presence in more than 100 countries around the world. The application of the data display and analysis technique by Miles and Huberman (1984, 1994) enables initial findings to be presented using the “dendogram” method, thereafter, leading to the development of a two-dimensional risk assessment matrix as the final result of this study.

People engage in a constant and reflexive process of categorizing others according to their race, gender, age or other salient social category. Decades of research have shown that social categorization often elicits stereotypes, prejudice, and discrimination. Social perception is complicated by the fact that people have multiple social identities and self-categorization with these identities can shift from one situation to another, coloring perceptions and evaluations of the self and others. This dissertation provides evidence that self-categorization with a novel group can override ostensible stable and pervasive racial biases in memory and evaluation and examines the neural substrates that mediate these processes. Experiment 1 shows that self-categorization with a novel mixed-race group elicited liking for ingroup members, regardless of race. This preference for ingroup members was mediated by the orbitofrontal cortex – a region of the brain linked to subjective valuation. Participants in novel groups also had greater fusiform and amygdala activity to novel ingroup members, suggesting that these regions are sensitive to the current self-categorization rather than features associated with race. Experiment 2 shows that preferences for ingroup members are evoked rapidly and spontaneously, regardless of race, indicating that ingroup bias can override automatic racial bias. Experiment 3 provides evidence that preferences for ingroup members are driven by ingroup bias rather than outgroup derogation. Experiment 4 shows that self-categorization increases memory for ingroup members eliminating the own-race memory bias. Experiment 5 provides direct evidence that fusiform activity to ingroup members is associated with superior memory for ingroup members. This study also shows greater amygdala activity to Black than White faces who are unaffiliated with either the ingroup or outgroup, suggesting that social categorization is flexible, shifting from group membership to race within a given social context. These five experiments illustrate that social perception and evaluation are sensitive to the current self-categorization – however minimal – and characterized by ingroup bias. This research also offers a relatively simple approach for erasing several pervasive racial biases. This multi-level approach extends several theories of intergroup perception and evaluation by making explicit links between self-categorization, neural processes, and social perception and evaluation.

People engage in a constant and reflexive process of categorizing others according to their race, gender, age or other salient social category. Decades of research have shown that social categorization often elicits stereotypes, prejudice, and discrimination. Social perception is complicated by the fact that people have multiple social identities and self-categorization with these identities can shift from one situation to another, coloring perceptions and evaluations of the self and others. This dissertation provides evidence that self-categorization with a novel group can override ostensible stable and pervasive racial biases in memory and evaluation and examines the neural substrates that mediate these processes. Experiment 1 shows that self-categorization with a novel mixed-race group elicited liking for ingroup members, regardless of race. This preference for ingroup members was mediated by the orbitofrontal cortex – a region of the brain linked to subjective valuation. Participants in novel groups also had greater fusiform and amygdala activity to novel ingroup members, suggesting that these regions are sensitive to the current self-categorization rather than features associated with race. Experiment 2 shows that preferences for ingroup members are evoked rapidly and spontaneously, regardless of race, indicating that ingroup bias can override automatic racial bias. Experiment 3 provides evidence that preferences for ingroup members are driven by ingroup bias rather than outgroup derogation. Experiment 4 shows that self-categorization increases memory for ingroup members eliminating the own-race memory bias. Experiment 5 provides direct evidence that fusiform activity to ingroup members is associated with superior memory for ingroup members. This study also shows greater amygdala activity to Black than White faces who are unaffiliated with either the ingroup or outgroup, suggesting that social categorization is flexible, shifting from group membership to race within a given social context. These five experiments illustrate that social perception and evaluation are sensitive to the current self-categorization – however minimal – and characterized by ingroup bias. This research also offers a relatively simple approach for erasing several pervasive racial biases. This multi-level approach extends several theories of intergroup perception and evaluation by making explicit links between self-categorization, neural processes, and social perception and evaluation.

A <i>web crawler</i> is a program that "walks" the Web to gather web resources. In order to scale to the ever-increasing Web, multiple crawling agents may be deployed in a distributed fashion to retrieve web data co-operatively. A common approach is to divide the Web into many partitions with an agent assigned to crawl within each one. If an agent obtains a web resource that is not from its partition, the resource will be transferred to the rightful owner. This thesis proposes a novel approach to distributed web data gathering by partitioning the Web into topics. The proposed approach employs multiple focused crawlers to retrieve pages from various topics. When a crawler retrieves a page of another topic, it transfers the page to the appropriate crawler. This approach is known as <i>topic-oriented collaborative web crawling</i>. An implementation of the system was built and experimentally evaluated. In order to identify the topic of a web page, a topic classifier was incorporated into the crawling system. As the classifier categorizes only English pages, a language identifier was also introduced to distinguish English pages from non-English ones. From the experimental results, we found that redundance retrieval was low and that a resource, retrieved by an agent, is six times more likely to be retained than a system that uses conventional hashing approach. These numbers were viewed as strong indications that <i>topic-oriented collaborative web crawling system</i> is a viable approach to web data gathering.

A <i>web crawler</i> is a program that "walks" the Web to gather web resources. In order to scale to the ever-increasing Web, multiple crawling agents may be deployed in a distributed fashion to retrieve web data co-operatively. A common approach is to divide the Web into many partitions with an agent assigned to crawl within each one. If an agent obtains a web resource that is not from its partition, the resource will be transferred to the rightful owner. This thesis proposes a novel approach to distributed web data gathering by partitioning the Web into topics. The proposed approach employs multiple focused crawlers to retrieve pages from various topics. When a crawler retrieves a page of another topic, it transfers the page to the appropriate crawler. This approach is known as <i>topic-oriented collaborative web crawling</i>. An implementation of the system was built and experimentally evaluated. In order to identify the topic of a web page, a topic classifier was incorporated into the crawling system. As the classifier categorizes only English pages, a language identifier was also introduced to distinguish English pages from non-English ones. From the experimental results, we found that redundance retrieval was low and that a resource, retrieved by an agent, is six times more likely to be retained than a system that uses conventional hashing approach. These numbers were viewed as strong indications that <i>topic-oriented collaborative web crawling system</i> is a viable approach to web data gathering.