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The evolution of super-resolution (SR) technology has seen significant advancements through the adoption of deep learning methods. However, the deployment of such models by resource-constrained devices necessitates models that not only perform efficiently, but also conserve computational resources. Binary neural networks (BNNs) offer a promising solution by minimizing the data precision to binary levels, thus reducing the computational complexity and memory requirements. However, for BNNs, an effective architecture is essential due to their inherent limitations in representing information. Designing such architectures traditionally requires extensive computational resources and time. With the advancement in neural architecture search (NAS), differentiable NAS has emerged as an attractive solution for efficiently crafting network structures. In this paper, we introduce a novel and efficient binary network search method tailored for image super-resolution tasks. We adapt the search space specifically for super resolution to ensure it is optimally suited for the requirements of such tasks. Furthermore, we incorporate Libra Parameter Binarization (Libra-PB) to maximize information retention during forward propagation. Our experimental results demonstrate that the network structures generated by our method require only a third of the parameters, compared to conventional methods, and yet deliver comparable performance. [ABSTRACT FROM AUTHOR]

This paper presents a Problem-Based Learning (PBL) approach to support and promote deeper student learning in a computer architecture course. A PBL approach using a simulator building activity was used to teach part of the course. The remainder of the course was taught using a traditional lecture-tutorial approach. Qualitative data was collected using a questionnaire survey to evaluate the effectiveness of the approach. The student responses showed that the PBL approach was successful in developing deeper learning experiences and general skills such as time management and teamwork while at the same time contributing to a more enjoyable learning experience. As part of the investigation, the authors also looked at student fears and concerns as they were asked to make a transition from a more structured to a less structured learning environment.

This paper presents a novel deep graph-based learning technique for speech emotion recognition which has been specifically tailored for energy efficient deployment within humanoid robots. Our methodology represents a fusion of scalable graph representations, rooted in the foundational principles of graph signal processing theories. By delving into the utilization of cycle or line graphs as fundamental constituents shaping a robust Graph Convolution Network (GCN)-based architecture, we propose an approach which allows the capture of relationships between speech signals to decode intricate emotional patterns and responses. Our methodology is validated and benchmarked against established databases such as IEMOCAP and MSP-IMPROV. Our model outperforms standard GCNs and prevalent deep graph architectures, demonstrating performance levels that align with state-of-the-art methodologies. Notably, our model achieves this feat while significantly reducing the number of learnable parameters, thereby increasing computational efficiency and bolstering its suitability for resource-constrained environments. This proposed energy-efficient graph-based hybrid learning methodology is applied towards multimodal emotion recognition within humanoid robots. Its capacity to deliver competitive performance while streamlining computational complexity and energy efficiency represents a novel approach in evolving emotion recognition systems, catering to diverse real-world applications where precision in emotion recognition within humanoid robots stands as a pivotal requisite. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent and learn strategies from expert players on different sides. Many game states can be expressed not only as a matrix data structure suitable for DCNN training but also as a graph data structure. Most of the available DCNN methods ignore the territory characteristics of both sides' positions based on the game rules. Therefore, in this paper, we propose a hybrid approach to the graph neural network to extract the features of the model of game-playing strategies and fuse it into a DCNN. As a graph learning model, graph convolutional networks (GCNs) provide a scheme by which to extract the features in a graph structure, which can better extract the features in the relationship between the game-playing strategies. We validate the work and design a hybrid network to integrate GCNs and DCNNs in the game of Go and show that on the KGS Go dataset, the performance of the hybrid model outperforms the traditional DCNN model. The hybrid model demonstrates a good performance in extracting the game strategy of Go. [ABSTRACT FROM AUTHOR]