Trust among individuals is essential for transactions. A human or software agent in need of resources may reduce transaction risk by modeling the trustworthiness of potential partners. Experience- and reputation-based trust models have unique advantages and disadvantages depending on environment factors, including availability of experience opportunities, trustee trustworthiness dynamics, reputation accuracy, and reputation cost. This research identifies how trusters may utilize both experience- and reputation-based trust modeling to achieve more accurate decision-making tools than using either modeling technique alone. The research produces: 1) the Adaptive Trust Modeling technique for combining experience- vs. reputation-based models to produce the most accurate aggregated model possible, 2) a quantitative analysis of the tradeoffs between experience- and reputation-based models to determine conditions under which each type of model is favorable, and 3) an Adaptive Cost Selection algorithm for assessing the value of trust information given acquisition costs. Experiments show that Adaptive Trust Modeling yields an aggregate trust model more accurate than either experience- or reputation-based modeling alone, and Adaptive Cost Selection acquires the optimal combination of trust information, maximizing a truster's transaction payoff while minimizing trust information costs. These tools enable humans and software agents to make effective trust-based decisions given dynamic system conditions.

The purpose of this research was to investigate object classification algorithms for the application of wheelchair interaction with the environment for the cognitively impaired wheelchair user. Towards this end, top performing object classification algorithms were trained on images of the target object classes (chair, dresser, and sink/washbasin) obtained from the internet and tested on images of the target object classes obtain in the home and patient room environments; these algorithms were Locality-constrained Linear Coding (LLC) [1], Kernel Descriptors (KDES) [2], and Hierarchical Matching Pursuit (HMP) [3]. It was found that HMP achieved the highest over classification accuracy (71.3%) in the home environment and LLC achieved the greatest accuracy (85.0%) in the patient room environment. This research also sought to investigate the potential of active learning to improve upon the obtained classification performance. A maximum mean classification accuracy of 98.6% was achieved when active learning was applied.

The purpose of this research was to investigate object classification algorithms for the application of wheelchair interaction with the environment for the cognitively impaired wheelchair user. Towards this end, top performing object classification algorithms were trained on images of the target object classes (chair, dresser, and sink/washbasin) obtained from the internet and tested on images of the target object classes obtain in the home and patient room environments; these algorithms were Locality-constrained Linear Coding (LLC) [1], Kernel Descriptors (KDES) [2], and Hierarchical Matching Pursuit (HMP) [3]. It was found that HMP achieved the highest over classification accuracy (71.3%) in the home environment and LLC achieved the greatest accuracy (85.0%) in the patient room environment. This research also sought to investigate the potential of active learning to improve upon the obtained classification performance. A maximum mean classification accuracy of 98.6% was achieved when active learning was applied.

No description available.

No description available.

Den här uppsatsen handlar om rörelsen intelligent design, som tror att en intelligent skapare ligger bakom stegen i evolutionen. På så vis blir inte evolutionen en slumpmässig sak, utan en sedermera intelligent designad evolution. Den här intelligenta skaparen är med andra ord Gud. Uppsatsens ämnar  undersöka intelligent design och gymnasieskolan, närmare bestämt hur ID korrelerar med didaktik. Rent konkret är syftet med uppsatsen att se hur man i de centrala didaktiska frågeställningarna bemöter intelligent design i religionsläroböcker på gymnasial nivå. De fyra frågeställningar uppsatsen utgår ifrån är följande : Vilken tyngd och utrymme får intelligent design i undervisningsmaterialet? Hur beskrivs intelligent design i jämförelse med hur bibelns skapelsetro eller kreationism framställs? Kan idén om intelligent design inspirera elevernas intresse och frågor om religion och tro? Varför ska man undervisa om intelligent design? För att besvara dessa frågor består huvuddelen av uppsatsen av ett litteraturstudium med hermeneutisk inriktning av tre läroböcker på gymnasialnivå. I bakgrunden tas även grundläggande information och historik om intelligent design upp. Av materialet som studeras kan jag dra slutsatsen att intelligent design får en relativt liten roll i undervisningsmaterialet och att intelligent design ofta sammanställs med kreationism. Förslag ges på hur intelligent design kan inspirera eleverna till intressanta diskussioner i ämnet religionskunskap genom att  erbjuda ett tredje perspektiv, en blandning av vetenskap och tro som kompletterar varandra. Utifrån detta och andra argument ges goda orsaker för att undervisa om ID i skolan.

Thesis (PhD)--University of South Australia, 1998

TRAFFIC congestions and accidents are major concerns in today’s transportation systems. This thesis investigates how to improve traffic throughput by reducing or eliminating bottlenecks on highways, in particular for merging situations such as intersections where a ramp leads onto the highway. In our work, cars are equipped with sensors that can measure distance to neighboring cars, and communicate their velocity and acceleration readings with one another. Sensor-enabled cars can locally exchange sensed information about the traffic and adapt their behavior much earlier than regular cars. / We propose proactive algorithms for merging different streams of sensor-enabled cars into a single stream. A proactive merging algorithm decouples the decision point from the actual merging point. Sensor-enabled cars allow us to decide where and when a car merges before it arrives at the actual merging point. This leads to a significant improvement in traffic flow as velocities can be adjusted appropriately. We compare proactive merging algorithms against the conventional priority-based merging algorithm in a controlled simulation environment. Experimental results show that proactive merging algorithms outperform the priority-based merging algorithm in terms of flow and delay. / More importantly, the imprecise information (errors in sensor measurements) is a major challenge for merging algorithms, because inaccuracies can potentially lead to unsafe merging behaviors. In this thesis, we investigate how the accuracy of sensors impacts merging algorithms, and design robust merging algorithms that tolerate sensor errors. Experimental results show that one of our proposed merging algorithms, which is based on the theory of time geography, is able to guarantee safe merging while tolerating two to four times more imprecise positioning information, and can double the road capacity and increase the traffic flow by 25%.

TRAFFIC congestions and accidents are major concerns in today’s transportation systems. This thesis investigates how to improve traffic throughput by reducing or eliminating bottlenecks on highways, in particular for merging situations such as intersections where a ramp leads onto the highway. In our work, cars are equipped with sensors that can measure distance to neighboring cars, and communicate their velocity and acceleration readings with one another. Sensor-enabled cars can locally exchange sensed information about the traffic and adapt their behavior much earlier than regular cars. / We propose proactive algorithms for merging different streams of sensor-enabled cars into a single stream. A proactive merging algorithm decouples the decision point from the actual merging point. Sensor-enabled cars allow us to decide where and when a car merges before it arrives at the actual merging point. This leads to a significant improvement in traffic flow as velocities can be adjusted appropriately. We compare proactive merging algorithms against the conventional priority-based merging algorithm in a controlled simulation environment. Experimental results show that proactive merging algorithms outperform the priority-based merging algorithm in terms of flow and delay. / More importantly, the imprecise information (errors in sensor measurements) is a major challenge for merging algorithms, because inaccuracies can potentially lead to unsafe merging behaviors. In this thesis, we investigate how the accuracy of sensors impacts merging algorithms, and design robust merging algorithms that tolerate sensor errors. Experimental results show that one of our proposed merging algorithms, which is based on the theory of time geography, is able to guarantee safe merging while tolerating two to four times more imprecise positioning information, and can double the road capacity and increase the traffic flow by 25%.

Thesis (M. Sc.)--University of Canterbury, 2007. / Typescript (photocopy). "June 2007." Includes bibliographical references (p. 117-135). Also available via the World Wide Web.