Development of adaptive training materials for conservation agriculture promotion in Africa.

In order for Conservation Agriculture (CA) to reach and impact small-scale farmers in Sub-Saharan Africa (SSA), CA technologies need to be adapted to suit the diversity of agroecological zones and cultures present on the continent. Training materials for CA promotion need to be similarly customizable to help extension staff and farmers develop their own, context-appropriate solutions from among the many possible CA approaches. From 2015 through 2018, a diverse set of farmer-level training materials for CA and complementary technologies was developed and field-tested by Canadian Foodgrains Bank partners. Together with a participatory, adaptive training methodology, these materials have enhanced the effectiveness of CA promotion, and they have been made available for copyright-free download in English, French, Kiswahili, Portuguese and Amharic (http://caguide.act-africa.org/, accessed 6 August 2021). This paper describes the process of developing these materials as well as challenges and constraints to their utilization.

Adding Pre-Training and Feedback Enhances the Benefits of Adaptive Flashcard Training

Adaptive training is tailored instruction designed to target an individual’s strengths and weaknesses, and it has been shown to improve learning outcomes and efficiency in a variety of domains, including flashcard training. The goal of this experiment was to determine whether the addition of other instructional strategies, such as enhanced feedback and pre-training, would further boost the learning benefits of an adaptive flashcard trainer. Thirty-nine college students learned to identify U.S. Navy ships using either a base version of the flashcard trainer, a version that included enhanced feedback, or a version that included pre-training and enhanced feedback. Results showed that the pre-training and enhanced feedback group significantly outperformed the other groups on delayed retention and transfer tests and showed higher learning efficiency. Implications for adaptive training and the Cognitive Theory of Multimedia Learning are discussed.

The effectiveness of adaptive training for stress inoculation in a simulated astronaut task

Astronauts operate in an environment with multiple hazards that can develop into life-threatening emergency situations. Managing stress in emergencies may require cognitive resources and lead to diminishing performance. Stress training aims to maintain performance under stress by methodically increasing stressor levels to build inoculation against stress. An adaptive virtual reality (VR) training system was developed with real-time stress detection by using machine learning on psychophysiological responses. Using a VR simulation of a spaceflight emergency fire, stress classifications were used to trigger adaptations of the VR environmental stressors (e.g., smoke, alarms, flashing lights), with the goal of maintaining a manageable level of stress during training. Fifty-seven healthy subjects underwent task training over eight trials with adaptive training (adaptive, n=19); results were compared to trials with predetermined gradual increases in stressors (graduated, n=18), and with trials with constant low-level stressors (skill-only, n=20). Stress responses were measured through heart rate, heart rate variability (i.e., root mean squared of successive differences (RMSSD), low frequency to high frequency (LF/HF) ratio), and task performance (distance-from-fire). Heart rate decreased and RMSSD increased pre-post training for all experimental conditions. The LF/HF ratio decreased pre-post training for the adaptive condition, but not in the other conditions. Results suggests that all conditions had lower stress, but the adaptive condition was more successful. Task performance showed a marginal increase across trials for the adaptive condition. Preliminary results suggest that training with the adaptive stress system can prepare individuals for responding to stressors better than skill-only and graduated training.

Memristor-CMOS Hybrid Neuron Circuit with Nonideal-Effect Correction Related to Parasitic Resistance for Binary-Memristor-Crossbar Neural Networks

Voltages and currents in a memristor crossbar can be significantly affected due to nonideal effects such as parasitic source, line, and neuron resistance. These nonideal effects related to the parasitic resistance can cause the degradation of the neural network’s performance realized with the nonideal memristor crossbar. To avoid performance degradation due to the parasitic-resistance-related nonideal effects, adaptive training methods were proposed previously. However, the complicated training algorithm could add a heavy computational burden to the neural network hardware. Especially, the hardware and algorithmic burden can be more serious for edge intelligence applications such as Internet of Things (IoT) sensors. In this paper, a memristor-CMOS hybrid neuron circuit is proposed for compensating the parasitic-resistance-related nonideal effects during not the training phase but the inference one, where the complicated adaptive training is not needed. Moreover, unlike the previous linear correction method performed by the external hardware, the proposed correction circuit can be included in the memristor crossbar to minimize the power and hardware overheads for compensating the nonideal effects. The proposed correction circuit has been verified to be able to restore the degradation of source and output voltages in the nonideal crossbar. For the source voltage, the average percentage error of the uncompensated crossbar is as large as 36.7%. If the correction circuit is used, the percentage error in the source voltage can be reduced from 36.7% to 7.5%. For the output voltage, the average percentage error of the uncompensated crossbar is as large as 65.2%. The correction circuit can improve the percentage error in the output voltage from 65.2% to 8.6%. Almost the percentage error can be reduced to ~1/7 if the correction circuit is used. The nonideal memristor crossbar with the correction circuit has been tested for MNIST and CIFAR-10 datasets in this paper. For MNIST, the uncompensated and compensated crossbars indicate the recognition rate of 90.4% and 95.1%, respectively, compared to 95.5% of the ideal crossbar. For CIFAR-10, the nonideal crossbars without and with the nonideal-effect correction show the rate of 85.3% and 88.1%, respectively, compared to the ideal crossbar achieving the rate as large as 88.9%.

Usage of Prosody Modification and Acoustic Adaptation for Robust Automatic Speech Recognition (ASR) System

Most of the automatic speech recognition (ASR) systems are trained using adult speech due to the less availability of the children's speech dataset. The speech recognition rate of such systems is very less when tested using the children's speech, due to the presence of the inter-speaker acoustic variabilities between the adults and children's speech. These inter-speaker acoustic variabilities are mainly because of the higher pitch and lower speaking rate of the children. Thus, the main objective of the research work is to increase the speech recognition rate of the Punjabi-ASR system by reducing these inter-speaker acoustic variabilities with the help of prosody modification and speaker adaptive training. The pitch period and duration (speaking rate) of the speech signal can be altered with prosody modification without influencing the naturalness, message of the signal and helps to overcome the acoustic variations present in the adult's and children's speech. The developed Punjabi-ASR system is trained with the help of adult speech and prosody-modified adult speech. This prosody modified speech overcomes the massive need for children's speech for training the ASR system and improves the recognition rate. Results show that prosody modification and speaker adaptive training helps to minimize the word error rate (WER) of the Punjabi-ASR system to 8.79% when tested using children's speech.

Speed versus damage: using selective feedback to modulate laparoscopic simulator performance

Background Adaptive training is an approach in which training variables change with the needs and traits of individual trainees. It has potential to mitigate the effect of personality traits such as impulsiveness on surgical performance. Selective performance feedback is one way to implement adaptive training. This paper investigates whether selective feedback can direct performance of trainees of either high- or low impulsiveness. Methods A total of 83 inexperienced medical students of known impulsiveness performed a four-session laparoscopic training course on a Virtual Reality Simulator. They performed two identical series of tasks every session. During one series of tasks they received performance feedback on duration and during the other series they received feedback on damage. Performance parameters (duration and damage) were compared between the two series of tasks to assess whether selective performance feedback can be used to steer emphasis in performance. To assess the effectiveness of selective feedback for people of high- or low impulsiveness, the difference in performance between the two series for both duration and damage was also assessed. Results Participants were faster when given performance feedback for speed for all exercises in all sessions (average z-value = − 4.14, all p values < .05). Also, they performed better on damage control when given performance feedback for damage in all tasks and during all sessions except for one (average z-value = − 4.19, all but one p value < .05). Impulsiveness did not impact the effectiveness of selective feedback. Conclusion Selective feedback on either duration or damage can be used to improve performance for the variable that the trainee receives feedback on. Trainee impulsiveness did not modulate this effect. Selective feedback can be used to steer training focus in adaptive training systems and can mitigate the negative effects of impulsiveness on damage control.

Formalization of the Burning Process of Virtual Reality Objects in Adaptive Training Complexes

Within the scope of this article, the problem of the formalization of physical processes in adaptive training complexes is considered on the example of virtual objects burning. Despite a fairly complete study of this process, the existing mathematical models are not adapted for the application in training complexes, which leads to a significant increase in costs and lower productivity due to the complexity of the calculations. Therefore, an adapted mathematical model is proposed that allows us to formalize the structure of virtual objects of burning, their basic properties and the processes of changing states, starting from the flame development of an object and ending with their complete destruction or extinguishment. The article proposes the use of threshold value diagrams and rules for changing the states of virtual reality objects to solve the problem of the formalization of burning processes. This tool is quite multi-purpose, which allows you to describe various physical processes, such as smoke, flooding, the spread of toxic gases, etc. The area of the proposed formalization approach includes the design and implementation of physical processes in simulators and multimedia complexes using virtual and augmented reality. Thus, the presented scientific research can be used to formalize the physical processes in adaptive training complexes for professional ergatic systems.