Using Simulations to Automatically Generate Authentic Constructivist Learning Environments

Abstract With increasingly short life-cycles, hand-crafting authentic constructivist environments for learning will become increasingly unfeasible. As the complexity of designed systems and devices increases, however, so does the role of simulations to support design. This paper presents an approach and an example of how naturally occurring simulations in design can be used to automatically generate authentic constructivist learning environments

Design and implementation of a low-cost classroom response system for a future classroom in the developing world

Economic considerations and lack of adequate infrastructure impose unique design constraints on future classrooms of the developing world. Thus, future classrooms in underprivileged nations may differ significantly from their counterparts in the developed world. Classroom response systems (CRS) are an emerging technology for the future classroom. CRS are wireless, hand-held devices that help students provide immediate feedback to questions posed by a teacher. In their present form, due to their relatively high cost and high infrastructural requirements, such systems are not sustainable in most developing countries. This paper presents the design and implementation of a CRS based on an open-source, low-cost, and easily manufactured hardware. The CRS design is based on a hybrid wireless/wired platform using Bluetooth with the 1-Wire networking technology. This design significantly reduces the cost, and is consistent with existing conditions in a typical developing country

An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge

Camera traps deployed in remote locations provide an effective method for ecologists to monitor and study wildlife in a non-invasive way. However, current camera traps suffer from two problems. First, the images are manually classified and counted, which is expensive. Second, due to manual coding, the results are often stale by the time they get to the ecologists. Using the Internet of Things (IoT) combined with deep learning represents a good solution for both these problems, as the images can be classified automatically, and the results immediately made available to ecologists. This paper proposes an IoT architecture that uses deep learning on edge devices to convey animal classification results to a mobile app using the LoRaWAN low-power, wide-area network. The primary goal of the proposed approach is to reduce the cost of the wildlife monitoring process for ecologists, and to provide real-time animal sightings data from the camera traps in the field. Camera trap image data consisting of 66,400 images were used to train the InceptionV3, MobileNetV2, ResNet18, EfficientNetB1, DenseNet121, and Xception neural network models. While performance of the trained models was statistically different (Kruskal–Wallis: Accuracy H(5) = 22.34, p < 0.05; F1-score H(5) = 13.82, p = 0.0168), there was only a 3% difference in the F1-score between the worst (MobileNet V2) and the best model (Xception). Moreover, the models made similar errors (Adjusted Rand Index (ARI) > 0.88 and Adjusted Mutual Information (AMU) > 0.82). Subsequently, the best model, Xception (Accuracy = 96.1%; F1-score = 0.87; F1-Score = 0.97 with oversampling), was optimized and deployed on the Raspberry Pi, Google Coral, and Nvidia Jetson edge devices using both TenorFlow Lite and TensorRT frameworks. Optimizing the models to run on edge devices reduced the average macro F1-Score to 0.7, and adversely affected the minority classes, reducing their F1-score to as low as 0.18. Upon stress testing, by processing 1000 images consecutively, Jetson Nano, running a TensorRT model, outperformed others with a latency of 0.276 s/image (s.d. = 0.002) while consuming an average current of 1665.21 mA. Raspberry Pi consumed the least average current (838.99 mA) with a ten times worse latency of 2.83 s/image (s.d. = 0.036). Nano was the only reasonable option as an edge device because it could capture most animals whose maximum speeds were below 80 km/h, including goats, lions, ostriches, etc. While the proposed architecture is viable, unbalanced data remain a challenge and the results can potentially be improved by using object detection to reduce imbalances and by exploring semi-supervised learning

An IoT-Based Services Infrastructure for Utility-Scale Distributed Solar Farms

Internet of Things (IoT) provides large-scale solutions for efficient resource monitoring and management. As such, the technology has been heavily integrated into domains such as manufacturing, healthcare, agriculture, and utilities, which led to the emergence of sustainable smart cities. The success of smart cities depends on the availability of data, as well as the quality of the data management infrastructure. IoT introduced numerous new software, hardware, and networking technologies designed for efficient and low-cost data transport, storage, and processing. However, proper selection and integration of the correct technologies is crucial to ensuring a positive return on investment for such systems. This paper presents a novel end-to-end infrastructure for solar energy analysis and prediction via edge-based analytics

Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning

Training machine learning and deep learning models for medical image classification is a challenging task due to a lack of large, high-quality labeled datasets. As the labeling of medical images requires considerable time and effort from medical experts, models need to be specifically designed to train on low amounts of labeled data. Therefore, an application of semi-supervised learning (SSL) methods provides one potential solution. SSL methods use a combination of a small number of labeled datasets with a much larger number of unlabeled datasets to achieve successful predictions by leveraging the information gained through unsupervised learning to improve the supervised model. This paper provides a comprehensive survey of the latest SSL methods proposed for medical image classification tasks

Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning

Training machine learning and deep learning models for medical image classification is a challenging task due to a lack of large, high-quality labeled datasets. As the labeling of medical images requires considerable time and effort from medical experts, models need to be specifically designed to train on low amounts of labeled data. Therefore, an application of semi-supervised learning (SSL) methods provides one potential solution. SSL methods use a combination of a small number of labeled datasets with a much larger number of unlabeled datasets to achieve successful predictions by leveraging the information gained through unsupervised learning to improve the supervised model. This paper provides a comprehensive survey of the latest SSL methods proposed for medical image classification tasks

Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning

Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SSL. Previous work in applying GANs to SSL are classified into pseudo-labeling/classification, encoder-based, TripleGAN-based, two GAN, manifold regularization, and stacked discriminator approaches. A quantitative and qualitative analysis of the various approaches is presented. The R3-CGAN architecture is identified as the GAN architecture with state-of-the-art results. Given the recent success of non-GAN-based approaches for SSL, future research opportunities involving the adaptation of elements of SSL into GAN-based implementations are also identified

Towards an Audio-based CNN for Classroom Observation on a Smartwatch

Classroom observation is an important tool to help achieve the United Nations\u27 fourth sustainable goal on quality and inclusive education. However, manually deploying this tool is expensive and not congruent with resource constraints in parts of the world where it is needed the most; Sub Saharan Africa and South and Central Asia. This paper presents the design of an initial implementation of an automated classroom observation system based on a convolutional neural network (CNN) which was optimized using the Hyperband approach. The system implements parts of the Stallings class observation system on a teacher\u27s smartwatch and uses audio data only. Based on \u27data in the wild\u27 collected in Pakistan, the CNN performed close to the level of human experts on unseen data (Cohen\u27s Kappa = 0.687 with human annotated data). F1-measure was 0.78 on unseen data. An Apple 4 smartwatch natively running the CNN was able to provide real-time inference (\u3c 1 second for 3 second audio segments)