Publications

We have developed a haptic virtual reality system for dental skill training. In this study we examined several kinds of kinematic information about the movement provided by the system supplement knowledge of results (KR) in dental skill acquisition. The kinematic variables examined involved force utilization (F) and mirror view (M). This created three experimental conditions that received augmented kinematic feedback (F, M, FM) and one control condition that did not (KR-only). Thirty-two dental students were randomly assigned to four groups. Their task was to perform access opening on the upper first molar with the haptic virtual reality system. An acquisition session consisted of two days of ten trials of practice in which augmented kinematic feedback was provided for the appropriate experimental conditions after each trial. One week after, a retention test consisting of two trials without augmented feedback was completed. The results showed that the augmented kinematic feedback groups had larger mean performance scores than the KR-only group in Day 1 of the acquisition and retention sessions (ANOVA, p<0.05). The apparent differences among feedback groups were not significant in Day 2 of the acquisition session (ANOVA, p>0.05). The trends in acquisition and retention sessions suggest that the augmented kinematic feedback can enhance the performance earlier in the skill acquisition and retention sessions.

The goal of dental education is to guide students' development through different stages from novice to competent, eventually resulting in an expert clinician. In this study we sought to identify process and outcome measures of clinical skill performance by comparing novices and experts using a virtual reality (VR) simulation system developed by our group.

In the last two decades, hand-in-hand with strong economic growth, Southeast Asia has experienced a strengthened academic community as well as an increase in public and private research and development. But, because the level of research activity and maturity of the research environment in Southeast Asian countries is varied and has been changing rapidly in recent years, public perceptions of the amount and relevance of the research output can often be inaccurate. This gives particular emphasis to the need for data to support decisions concerning collaborative research programmes.

Two relevant recent developments in the area of science and technology (S&T) and related policy-making motivate this article: First, bibliometric data on a specific research area’s performance becomes an increasingly relevant source for S&T policy-making and evaluation. This trend is embedded in wider discussions on evidence-based policy-making. Secondly, the scientific output of Southeast Asian countries is rising, as is the number of international research collaborations with the second area of our interest: Europe. Against this background, we employ basic bibliometric methodology in order to draw a picture of Southeast Asian research strengths as well the amount and focus of S&T cooperation between the countries in Southeast Asia and the European Union. The results can prove useful for an interested public as well as for the scientific community and science, technology and innovation policy-making.

Independent intellectual creative capacity developed through research is essential in enabling countries to take control of exploring, planning, and implementing their own most appropriate sustainable development paths. Given the scarce financial resources in many countries, areas of research focus must be carefully chose so as to achieve maximum value from investment at a level that will have noticeable positive impact upon society. In pursuit of its core mission of providing countries and other stakeholders with the tools they need to better assess and develop their own research capacity, making the most of scarce resources, United Nations University International Institute for Software Technology (UNU-IIST) has launched programs to support this. Among these programs, a key project is the Global Research Benchmarking System (GRBS) which provides objective data and analyses to benchmark research performance in traditional disciplinary subject areas and in interdisciplinary areas for the purpose of strengthening the quality and impact of research. This paper presents case studies to illustrate the use of GRBS. The case studies show that the GRBS can help the universities to identify niche areas in which they can excel, to make more rational strategic resource allocation decisions, and to publicize program strengths. Finally the paper discusses that how a university can improve its position among its peers by using the research quality and output indicators proposed by the GRBS.

We introduce a new quantitative measure of international scholarly impact of countries by using bibliometric techniques based on publication and citation data. We present a case study to illustrate the use of our proposed measure in the subject area Energy during 1996 to 2009. We also present geographical maps to visualize knowledge flows among countries. Finally, using correlation analysis between publication output and international scholarly impact, we study the explanatory power of the applied measure.

This paper discusses the problem of abstract­ ing conditional probabilistic actions. We identify two distinct types of abstraction: intra­action abstraction and inter­action ab­ straction. We define what it means for the abstraction of an action to be correct and then derive two methods of intra­action ab­ straction and two methods of inter­action ab­ straction which are correct according to this criterion. We illustrate the developed tech­ niques by applying them to actions described with the temporal action representation used in the drips decision­theoretic planner and we describe how the planner uses abstraction to reduce the complexity of planning.

An intelligent reactive planning agent for partially ob­ servable stochastic domains requires a number of di­ verse capabilities. First, the agent must be able to intelligently allocate its resources. This means that it must be able to decide how much time to allocate to deliberation in a way that is responsive to the environ­ ment in which the agent finds itself. It must also be able to decide when to sense and how much time and effort to spend sensing.

Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician.

We define a language for representing context­sensitive probabilistic knowledge. A knowledge base consists of a set of universally quantified probability sentences that include context constraints, which allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a query answering procedure which takes a query Q and a set of evidence E and constructs a Bayesian network to compute P (QjE). The posterior probability is then computed using any of a number of Bayesian network inference algorithms. We use the declarative semantics to prove the query procedure sound and complete. We use concepts from logic programming to justify our approach.