Queue-based agent communication architecture for intelligent human-like computer interfaces

Human-like computer interaction systems requires far more than just simple speech input/output. Such a system should communicate with the user verbally, using a conversational style language. It should be aware of its surroundings and use this context for any decisions it makes. As a synthetic character, it should have a computer generated human-like appearance. This, in turn, should be used to convey emotions, expressions and gestures. Finally, and perhaps most important of all, the system should interact with the user in real time, in a fluent and believable manner.

Investigating familiar interfaces to help older adults learn computer applications more easily

Many older adults wish to gain competence in using a computer, but many application interfaces are perceived as complex and difficult to use, deterring potential users from investing the time to learn them. Hence, this study looks at the potential of ‘familiar’ interface design which builds upon users’ knowledge of real world interactions, and applies existing skills to a new domain. Tools are provided in the form of familiar visual objects, and manipulated like real-world counterparts, rather than with buttons, icons and menus found in classic WIMP interfaces. This paper describes the formative evaluation of computer interactions that are based upon familiar real world tasks, which supports multitouch interaction, involves few buttons and icons, no menus, no right-clicks or double-clicks and no dialogs. Using an example of an email client to test the principles of using “familiarity”, the initial feedback was very encouraging, with 3 of the 4 participants being able to undertake some of the basic email tasks with no prior training and little or no help. The feedback has informed a number of refinements of the design principles, such as providing clearer affordance for visual objects. A full study is currently underway.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

An optimized ERP Brain-computer interface based on facial expression changes

OBJECTIVE: Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern. APPROACH: Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures. MAIN RESULTS: The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05). SIGNIFICANCE: The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.

Multilevel Training of Binary Morphological Operators

The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results.

Computer and IT skills of Australian first-year university undergraduate students

The use of computers and information technology is becoming more widespread in chemical education and in the wider community and workforce. Universities are increasing the use of computers and information technology in their teaching and learning programs. The Australian situation, reported here, can be viewed as a microcosm, reflecting a world-wide trend.

The penetration of general IT literacy and ability amongst Australian undergraduate students is rising rapidly. This paper reports surveys of the detailed IT skills of Australian undergraduate students. To the best of our knowledge, apart from our own work, there have been no other published surveys of specific IT skills. Most students have some reasonable computer skills at the start of their university studies, but the level of skill is not uniformly high. In fact, many IT literate students lack a sufficient level of skill to use the new technologies, including full use of web-based flexible learning. There is an urgent need for IT training for university students in order to achieve successful learning outcomes using IT and to satisfy the needs of future employers.

This article, which is the second in a series (1) of investigations on the computer skills of undergraduate students at the start of university, is an expanded version of a paper presented at the World Chemistry Congress held in Brisbane (Australia) during 1-6 July 2001.

Legal LEGO: Model based computer assisted teaching in evidence courses

This paper describes the development of a new approach to the use of ICT for the teaching of courses in the interpretation and evaluation of evidence. It is based on ideas developed for the teaching of science to school children, in particular the importance of models and qualitative reasoning skills. In the first part, we make an analysis of the basis of current research into “evidence scholarship” and the demands such a system would have to meet. In the second part, we introduce the details of such a system that we developed initially to assist police in the interpretation of evidence.

A knowledge-based economy landscape: implications for tertiary education and research training in Australia

This paper discusses the higher education sector’s role in knowledge-based economy though research training, that is, doctoral education. It also examines how a Faculty of Education supports its doctoral candidates in their endeavours to become ‘knowledge producers’. Two themes are explored: one is Australia’s limited investment in education by international standards; and the other is the research training needs and circumstances of doctoral candidates who are located in professional and workplace contexts. The paper discusses the role of online support and a Doctoral Studies in Education (DSE) online seminar program to support primarily off-campus, part-time mid-career professionals. These are typical of many of Australia’s doctoral candidates. E-learning is examined as part of a comprehensive support and research training strategy for doctoral candidates studying at a distance. We discuss the sorts of opportunities and experiences our candidates receive and the extent to which they are readied to work effectively in a knowledge-based economy.

Community center-based resistance training for the maintenance of glycemic control in adults with type 2 diabetes

OBJECTIVE--The purpose of this study Was to determine whether beneficial effects on glycemic control of an initial laboratory-supervised resistance training program could be sustained through a community center-based maintenance program.

RESEARCH DESIGN AND METHODS--We studied 57 overweight (BMI [greater than or equal to] 27 kg/[m.sup.2]) sedentary men and women aged 40-80 years with established (>6 months) type 2 diabetes. Initially, all participants attended a twice-weekly 2-month supervised resistance training program conducted in the exercise laboratory. Thereafter, participants undertook a resistance training maintenance program (2 times/week) for 12 months and were randomly assigned to carry this out either in a community fitness and recreation center (center) or in their domestic environment (home). Glycemic control ([HbA.sub.1c] [A1C]) was assessed at 0, 2, and 14 months.

RESULTS--Pooling data from the two groups for the 2-month supervised resistance training program showed that compared with baseline, mean A1C fell by -0.4% [95% CI -0.6 to -0.2]. Within-group comparisons showed that A1C remained lower than baseline values at 14 months in the center group (-0.4% [-0.7 to -0.03]) but not in the home group (-0.1% [-0.4 to 0.3]). However, no between-group differences were observed at each time point. Changes in A1C during the maintenance period were positively associated with exercise adherence in the center group only.

CONCLUSIONS--Center-based but not home-based resistance training was associated with the maintenance of modestly improved glycemic control from baseline, which was proportional to program adherence. Our findings emphasize the need to develop and test behavioral methods to promote healthy lifestyles including increased physical activity in adults with type 2 diabetes.

Effect of a low-resource-intensive lifestyle modification program incorporating gymnasium-based and home-based resistance training on type 2 diabetes risk in Australian adults

OBJECTIVE--The purpose of this study was to assess the effectiveness of a low-resource-intensive lifestyle modification program incorporating resistance training and to compare a gymnasium-based with a home-based resistance training program on diabetes diagnosis status and risk.

RESEARCH DESIGN AND METHODS--A quasi-experimental two-group study was undertaken with 122 participants with diabetes risk factors; 36.9% had impaired glucose tolerance (1GT) or impaired fasting glucose (IFG) at baseline. The intervention included a 6-week group self-management education program, a gymnasium-based or home-based 12-week resistance training program, and a 34-week maintenance program. Fasting plasma glucose (FPG) and 2-h plasma glucose, blood lipids, blood pressure, body composition, physical activity, and diet were assessed at baseline and week 52.

RESULTS--Mean 2-h plasma glucose and FPG fell by 0.34 mmol/1 (95% CI--0.60 to--0.08) and 0.15 mmol/l (-0.23 to -0.07), respectively. The proportion of participants with IFG or IGT decreased from 36.9 to 23.0% (P = 0.006). Mean weight loss was 4.07 kg (-4.99 to -3.15). The only significant difference between resistance training groups was a greater reduction in systolic blood pressure for the gymnasium-based group (P = 0.008).

CONCLUSIONS--This intervention significantly improved diabetes diagnostic status and reduced diabetes risk to a degree comparable to that of other low-resource-intensive lifestyle modification programs and more intensive interventions applied to individuals with IGT. The effects of home-based and gymnasium-based resistance training did not differ significantly.

Hierarchical development of training database for artificial neural network-based damage identification

Though serving as an effective means for damage identification, the capability of an artificial neural network (ANN) for quantitative prediction is substantially dependent on the amount of training data. In virtue of a concept of “Digital Damage Fingerprints” (DDF), a hierarchical approach for the development of training databases was proposed for ANN-based damage identification. With the object of exploiting the capability of ANN to address the key questions: “Is there damage?” and “Where is the damage?”, the amount of training data (damage cases) was increased progressively. Mutuality was established between the quantity of training data and the accuracy of answers to the two questions of interest, and was experimentally validated by identifying the position of actual damage in carbon fibre-reinforced composite laminates. The results demonstrate that such a hierarchical approach is capable of offering prediction as to the presence and location of damage individually, with substantially reduced computational cost and effort in the development of the ANN training database.

Early breast cancer identification : Which way to go? Microarray or image based computer aided diagnosis!

The goal of this research is to develop a computer aided diagnostic (CAD) system that can detect breast cancer in the early stage by using microarray and image data. We verified the performance of six well known classification algorithms with various performance matrices. Although we do not suggest a unique classifier algorithm for a CAD system, we do identify a number of algorithms whose performance is very promising. The algorithms performance was validated by 3 images dataset; two have been used for the first time in this experiment. Multidimensional image filtering is adopted for the final data extraction. The image data classification performance is compared with microarray data. Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach.

User-centered evaluation of a virtual environment training system : utility of user perception measures

This study assessed the utility of measures of Self-efficacy (SelfEfficacy) and Perceived VE efficacy (PVEefficacy) for quantifying how effective VEs are in procedural task training. SelfEfficacy and PVEefficacy have been identified as affective construct potentially underlying VE efficacy that is not evident from user task performance. The motivation for this study is to establish subjective measures of VE efficacy and investigate the relationship between PVEefficacy, SelfEfficacy and User task performance. Results demonstrated different levels of prior experience in manipulating 3D objects in gaming or computer environment (LOE3D) effects on task performance and user perception of VE efficacy. Regression analysis revealed LOE3D, SelfEfficacy,
PVEefficacy explain significant portions of the variance in VE efficacy. Results of the study provide further evidence that task performance may share relationships with PVEefficacy and SelfEfficacy, and that affective constructs, such as PVEefficacy, and SelfEfficacy may serve as alternative, subjective measures of task performance that account for VE efficacy.