Further, a fair number of Europeans have American Ph.D.'s and tend to maintain
their ties to the United States. The better U.S. universities and research
laboratories usually have at least one European visitor in computer sciences
each year, and often more. The American conferences on theory of computation,
principles of programming languages, and so on, are well attended by
Europeans. In addition, the major research journals, such as the Journal of the
Association for Computing ...
... memudahkan y + 112 Struktur Matematik Diskret. Sifat operasi + Tukar Tertib x
+ y = y + x Sekutuan (x + y) + z = x + (y + z) Taburan x + (y • z) = (x + y) • (x + z)
Identiti x + 0 = x Pelengkap x + x' = 1 Idempoten X + X = X Hukum De Morgan (x +
...
This project examines research-based education, which includes research-led education, in the discipline of computer science at the Australian National University. It traces the University's position on the issue over recent years, and it explores and evaluates current practices of research-based education. Of particular interest is the newly-introduced Bachelor of Computer Science program, which is found to have many issues in common with the University's flagship research-oriented degree, the Bachelor in Philosophy (PhB) program. From the analysis of data from semi-structured interviews with both BCS students and academics either involved in the BCS and/or whose teaching practices relate to research-based education, this project explores the range of staff and student attitudes and experiences in this area. It then identifies challenges and opportunities in creating an expanded and more coherent use of research-based education through the computer science curriculum. This project makes the following main conclusions. Firstly, that the range of staff and student attitudes and experiences concur with what is found in recent literature. Secondly, there are several important lessons for the BCS program and research-based education in general that can be gleaned from the PhB experience. Thirdly, that there are in fact already a large range of practices of research-based education in computer science (but there has previously been little overall awareness of this). Finally, that if applied judiciously, the potential benefits of research-based education are considerable, in terms of higher quality pedagogical outcomes, enhanced student and academic experience and a positive future impact in computing-related industry.
This project examines research-based education, which includes research-led education, in the discipline of computer science at the Australian National University.
The purpose of this thesis is to propose new methodology for data normalization and cluster prediction in order to help us unravel the structure of a data set. Such data may come from many different areas, for example clinical responses, genomic multivariate data such as microarray, educational test scores, and so on. In addition and more specifically for clinical trials this thesis proposes a new cohort size adaptive design method that will adapt cohort size eventually and finally will save time and cost while still keep the accuracy to find the target maximum tolerate dose. The new normalization method is called Fishe-Yates normalization and it has the advantage of being computationally superior than the standard quantile normalization and it improved the power of the following statistical analysis. Once the data has been normalized the observations are clustered by their pattern of response and cluster prediction is used to validate the findings. We propose a new method for cluster prediction which is a natural way to predict for hierarchical clustering. Our prediction method using nonlinear boundaries between clusters. Normalization method and clustering prediction method can help to identify subgroups of patients which has positive treatment effect. For clinical trial study, this thesis also proposes a new adaptive design which will adapt cohort size thus save time and cost to locate the target maximum tolerated dose.
The purpose of this thesis is to propose new methodology for data normalization and cluster prediction in order to help us unravel the structure of a data set.
