Face-to-face communication is very important skill to share intentions. However, many people in the modem world feel that they are deficient in face-to-face communication. So, we feel that it is necessary to support t...Face-to-face communication is very important skill to share intentions. However, many people in the modem world feel that they are deficient in face-to-face communication. So, we feel that it is necessary to support their face-to-face communication using information technologies. We have developed a topic-providing system that can infer behaviors from daily life and provides users with information about their conversation partner, including that on his hometown, hobbies and life logs when face-to-face communication is initiated. The life logs are details about a user＇s life, and are generated using a Bayesian network on the basis of sensor data provided by our system. This system enables users to access other users＇ information of behaviors from the accumulated life logs and it utilizes this infbrmation to generate topics for conversation. We evaluated the accuracy with which proposal system inferred behaviors to confirm whether exact life log generation is possible. And we also evaluated the proposed system by administering a questionnaire to confirm whether the proposed system can support face-to-face communication.展开更多
Highway traffic safety is an issue confronting developing countries and those of industrialized nations. Nigeria, as a developing country, has been experiencing unusually high traffic related injury and fatality rates...Highway traffic safety is an issue confronting developing countries and those of industrialized nations. Nigeria, as a developing country, has been experiencing unusually high traffic related injury and fatality rates. In order to commence combating highway safety problems in Nigeria, the first task is to identify the major contributing factors; however, Nigeria has no reliable and comprehensive database of traffic accidents and casualties. Consequently, the Delphi technique was utilized in generating the required data such as number of registered automobiles, number of licensed drivers, and annual fatality count for modeling and forecasting accident rates in Nigeria. A Bayesian network model was developed and used, with the data obtained from Delphi process, to demonstrate possible traffic safety responses to different scenarios of changes in the Nigerian socio-political culture. Although the Delphi technique and the Bayesian network model only estimate the accident and safety data, those methods can be a realistic option when those data are not available, especially for the developing countries. As a result, the major accident contributors have been identified and the top three contributors-road condition, DUI （driving under the influence） and reckless driving-are policy related. The Nigerian traffic safety outlook would improve significantly if the existing laws and policies can be enforced, even at a very moderate level.展开更多
Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically infe- rior, stable community. Such regime shifts are especially common in shallow marine communities, ...Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically infe- rior, stable community. Such regime shifts are especially common in shallow marine communities, such as the transition of kelp forests to algal turfs that harbour far lower biodiversity. Stable regimes in communities are a result of balanced interactions be- tween species, and predicting new regimes therefore requires an evaluation of new species interactions, as well as the resilience of the ＇stable＇ position. While computational optimisation techniques can predict new potential regimes, predicting the most likely community state of the various options produced is currently educated guess work. In this study we integrate a stable regime op- timisation approach with a Bayesian network used to infer prior knowledge of the likely stress of climate change （or, in practice, any other disturbance） on each component species of a representative rocky shore community model. Combining the results, by calculating the product of the match between resilient computational predictions and the posterior probabilities of the Bayesian network, gives a refined set of model predictors, and demonstrates the use of the process in determining community changes, as might occur through processes such as climate change. To inform Bayesian priors, we conduct a review of molecular approaches applied to the analysis of the transcriptome of rocky shore organisms, and show how such an approach could be linked to meas- ureable stress variables in the field. Hence species-specific microarrays could be designed as biomarkers of in situ stress, and used to inform predictive modelling approaches such as those described here.展开更多
文摘Face-to-face communication is very important skill to share intentions. However, many people in the modem world feel that they are deficient in face-to-face communication. So, we feel that it is necessary to support their face-to-face communication using information technologies. We have developed a topic-providing system that can infer behaviors from daily life and provides users with information about their conversation partner, including that on his hometown, hobbies and life logs when face-to-face communication is initiated. The life logs are details about a user＇s life, and are generated using a Bayesian network on the basis of sensor data provided by our system. This system enables users to access other users＇ information of behaviors from the accumulated life logs and it utilizes this infbrmation to generate topics for conversation. We evaluated the accuracy with which proposal system inferred behaviors to confirm whether exact life log generation is possible. And we also evaluated the proposed system by administering a questionnaire to confirm whether the proposed system can support face-to-face communication.
文摘Highway traffic safety is an issue confronting developing countries and those of industrialized nations. Nigeria, as a developing country, has been experiencing unusually high traffic related injury and fatality rates. In order to commence combating highway safety problems in Nigeria, the first task is to identify the major contributing factors; however, Nigeria has no reliable and comprehensive database of traffic accidents and casualties. Consequently, the Delphi technique was utilized in generating the required data such as number of registered automobiles, number of licensed drivers, and annual fatality count for modeling and forecasting accident rates in Nigeria. A Bayesian network model was developed and used, with the data obtained from Delphi process, to demonstrate possible traffic safety responses to different scenarios of changes in the Nigerian socio-political culture. Although the Delphi technique and the Bayesian network model only estimate the accident and safety data, those methods can be a realistic option when those data are not available, especially for the developing countries. As a result, the major accident contributors have been identified and the top three contributors-road condition, DUI （driving under the influence） and reckless driving-are policy related. The Nigerian traffic safety outlook would improve significantly if the existing laws and policies can be enforced, even at a very moderate level.
文摘Ecological regime shift is the rapid transition from one stable community structure to another, often ecologically infe- rior, stable community. Such regime shifts are especially common in shallow marine communities, such as the transition of kelp forests to algal turfs that harbour far lower biodiversity. Stable regimes in communities are a result of balanced interactions be- tween species, and predicting new regimes therefore requires an evaluation of new species interactions, as well as the resilience of the ＇stable＇ position. While computational optimisation techniques can predict new potential regimes, predicting the most likely community state of the various options produced is currently educated guess work. In this study we integrate a stable regime op- timisation approach with a Bayesian network used to infer prior knowledge of the likely stress of climate change （or, in practice, any other disturbance） on each component species of a representative rocky shore community model. Combining the results, by calculating the product of the match between resilient computational predictions and the posterior probabilities of the Bayesian network, gives a refined set of model predictors, and demonstrates the use of the process in determining community changes, as might occur through processes such as climate change. To inform Bayesian priors, we conduct a review of molecular approaches applied to the analysis of the transcriptome of rocky shore organisms, and show how such an approach could be linked to meas- ureable stress variables in the field. Hence species-specific microarrays could be designed as biomarkers of in situ stress, and used to inform predictive modelling approaches such as those described here.
