An empirical examination of feedback : user control and performance in a hapto-audio-visual training environment

Utilising advanced technologies, such as virtual environments (VEs), is of importance to training and education. The need to develop and effectively apply interactive, immersive 3D VEs continues to grow. As with any emerging technology, user acceptance of new software and hardware devices is often difficult to measure and guidelines to introduce and ensure adequate and correct usage of such technologies are lacking. It is therefore imperative to obtain a solid understanding of the important elements that play a role in effective learning through VEs. In particular, 3D VEs may present unusual and varied interaction and adoption considerations. The major contribution of this study is to investigate a complex set of interrelated factors in the relatively new sphere of VEs for training and education. Although many of the factors appears to be important from past research, researcher have not explicitly studied a comprehensive set of inter-dependant, empirically validated factors in order to understand how VEs aid complex procedural knowledge and motor skill learning. By integrating theory from research on training, human computer interaction (HCI), ergonomics and cognitive psychology, this research proposes and validates a model that contributes to application-specific VE efficacy formation. The findings of this study show visual feedback has a significant effect on performance. For tactile/force feedback and auditory feedback, no significant effect were found. For satisfaction, user control is salient for performance. Other factors such as interactivity and system comfort, as well as level of task difficulty, also showed effects on performance.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Computer reminders for chlamydia screening in general practice : a randomized controlled trial

Background: Chlamydia notifications are increasing in Australia, and the use of a computer alert prompting general practitioners to test young women is a potential way to increase opportunistic chlamydia testing. The aim of this trial was to determine the effectiveness of a computer alert in general practice on chlamydia testing in young women.

Methods: In 2006, clinics (n = 68) in Melbourne, Australia were cluster randomized into 2 groups: the intervention group received a computerized alert advising the general practitioner to discuss chlamydia testing with their patient which popped up when the medical record of a 16- to 24-year-old woman was opened; the control group received no alert. The outcome was whether or not that patient received a chlamydia test at the level of a single consultation with an eligible patient. A mixed effects logistic regression model adjusting for clustering was used to assess the impact of the alert on the proportion of women tested for chlamydia during the trial period.

Results: Testing increased from 8.3% (95% confidence interval (CI): 6.8, 9.8) to 12.2% (95% CI: 9.1, 15.3) (P < 0.01) in the intervention group, and from 8.8% (95% CI: 6.8, 10.7) to 10.6% (95% CI: 8.5, 12.7) (P < 0.01) in the control group. Overall, the intervention group had a 27% (OR = 1.3; 95% CI: 1.1, 1.4) greater increase in testing.

Conclusion: The results of this study suggest that alerts alone may not be sufficient to get chlamydia testing levels up sufficiently high enough to have an impact on the burden of chlamydia in the population but that they could be included as part of a more complex intervention.

Counterfactual thinking and anticipated emotions enhance performance in computer skills training

The present study examined the relationship between novice learners’ counterfactual thinking (i.e. generating ‘what if’ and ‘if only’ thoughts) about their initial training experience with a computer application and subsequent improvement in task performance. The role of anticipated emotions towards goal attainment in task performance was also assessed. Undergraduate students (N ¼ 42) with minimal experience in using computer spreadsheets underwent basic training in using Microsoft Excel. All participants were assessed on their anticipated positive and negative emotions regarding goal attainment at the outset. After completing their first task, participants allocated to a counterfactual condition received instructions to generate counterfactual thoughts regarding their initial task performance, whereas participants in a control condition did not. The counterfactual group showed only marginally greater improvement in task performance (measured by task completion time and accuracy) than the control group.  However, we also found that positive anticipated emotions were associated with improvement in task performance but for the counterfactual group only. Our data have implications for incorporating counterfactual thinking into information technology skills training to enhance learning outcomes for novice learners.

Provision of control measures for exposure of the hands to wet-working conditions in Australian workplaces

Objective: To analyze the occupational and demographic characteristics for workers participating in the Australian National Hazard Exposure Worker Surveillance (NHEWS) Survey, who reported the provision of various types of workplace control measures for exposure of the hands to wet-working conditions, and to identify the barriers for the provision of controls. Methods: Computer-assisted telephone interviews were conducted with 4500 workers in 2008. Workers were asked about the types of control measures provided to them in the workplace for exposure of the hands to liquids. Results: Workplace size was the strongest predictor for the provision of control measures. Compared to workplaces with fewer than five employees, workers in workplaces with 200 or more employees were more likely to report provision of gloves, barrier creams and moisturizers, labeling and warning, and ongoing training and education about skin care. Conclusion: Smaller workplaces have poorer access to control measures to mitigate exposure to wet work.

A sliding-mode-control-based adaptive cruise controller

This paper presents an alternative solution to the conventional cruise controller of a hybrid electric vehicle based on the sliding mode control approach. The mathematical model of a hybrid electric vehicle cruise control system is developed. Then, the sliding mode control approach is applied as the controller. The sliding mode control stability is investigated and demonstrated. Thereafter, the system is simulated and the results are presented. © 2014 IEEE.

Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.

Improved processing speed: online computer-based cognitive training in older adults

In an increasingly aging population, a number of adults are concerned about declines in their cognitive abilities. Online computer-based cognitive training programs have been proposed as an accessible means by which the elderly may improve their cognitive abilities; yet, more research is needed in order to assess the efficacy of these programs. In the current study, a commercially available 21-day online computer-based cognitive training intervention was administered to 34 individuals aged between 53 and 75 years. The intervention consisted of computerized training in reaction time, inspection time, short-term memory for words, executive function, visual spatial acuity, arithmetic, visual spatial memory, visual scanning/discrimination, and n-back working memory. An active solitaire control group was also included. Participants were tested at baseline, posttraining and at three-weeks follow-up using a battery of neuropsychological outcome measures. These consisted of simple reaction time, complex reaction time, digit forwards and backwards, spatial working memory, digit symbol substitution, RAVLT, and trail making. Significant improvement in simple reaction time and choice reaction time task was found in the cognitive training group both posttraining and at three-weeks follow-up. However, no significant improvements on the other cognitive tasks were found. The training program was found to be successful in achieving transfer of trained cognitive abilities in speed of processing to similar untrained tasks. © 2012 Copyright Taylor and Francis Group, LLC.

Keyboard control method for virtual reality micro-robotic cell injection training

The rapid development of virtual reality offers significant potential for skills training applications. Our ongoing work proposes virtual reality operator training for the micro-robotic cell injection procedure. The interface between the operator and the system can be achieved in many different ways. The computer keyboard is ubiquitous in its use for everyday computing applications and also commonly utilized in virtual reality systems. Based on the premise that most people have experience in using a computer keyboard, as opposed to more sophisticated input devices, this paper considers the feasibility of using a keyboard to control the micro-robot for cell injection. In this study, thirteen participants underwent the experimental evaluation. The participants were asked to perform three simulated trial sessions in a virtual micro-robotic cell injection environment. Each session consisted of ten cell injection trials and relevant data for each trial were recorded and analyzed. Results showed participants' performance improvement after the three sessions. It was also observed that participants intuitively controlled multiple axes of the micro-robot simultaneously despite the absence of instruction on how to do so. This continued throughout the experiments and suggests skills transfer from other keyboard based interactions. Based on the results provided, it is suggested that keyboard control is a feasible, simple and low-cost control method for the virtual micro-robot.

Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.

Spiking neural network-based target tracking control for autonomous mobile robots

In this paper, a target tracking controller based on spiking neural network is proposed for autonomous robots. This controller encodes the preprocessed environmental and target information provided by CCD cameras, encoders and ultrasonic sensors into spike trains, which are integrated by a three-layer spiking neural network (SNN). The outputs of SNN are generated based on the competition between the forward/backward neuron pair corresponding to each motor, with the weights evolved by the Hebbian learning. The application to target tracking of a mobile robot in unknown environment verifies the validity of the proposed controller.

Multi-surface sliding control of MIMO autonomous flight systems

In this paper, we present a hardware in the loop simulation of our proposed multi-surface sliding control (MSSC) for trajectory tracking of 6 degrees of freedom (6-DOF) inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). Using MSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. The control law is implemented on an onboard computer and is validated though Hardware-In-the-Loop (HIL) simulations, between the hardware and the flight simulator X-Plane, which simulates the unmanned aircraft dynamics, sensors, and actuators. Simulation results are presented to validate the analysis.