Tactile perception is a crucial channel for education in individuals with blindness and low vision (BLV), and beadwork is a low-cost and widely adopted tool in their educational practices. In this paper, we aim to explore what the field of Human-Computer Interaction (HCI) can learn from beadwork practices in relation to educational somatic experiences and tangible interaction. To understand how beadwork practices are enacted, we conducted in-class observations, semi-structured interviews, and focus groups with BLV students and teachers. Our results suggest that beadwork is an effective tool to foster personal development (e.g., mathematical and creativity skills) and social engagement (e.g., career development). Based on our findings, we offer insights into how beadwork can serve as a cost-effective material for HCI, particularly in the context of embodied cognition and soma design. Finally, we propose how state-of-the-art technology could be integrated to optimize the overall process.

Building energy prediction plays critical roles in the study of green building and smart city. The most challenging issue is to predict energy demand profiles over multiple time steps, which may have inconsistent timescales. Due to complex temporal dependence, existing prediction approaches cannot satisfy certain requirements in multistep (MS) or multitimescale (MTS) applications. In this article, a deep-chain echo state network (DCESN) is proposed to enhance the mapping capability for the MS demand prediction. The DCESN composed of many submodules of echo state network (ESN) belongs to the single-input multi-output (SIMO) model, and neuron states generated by sequential steps are utilized to prevent accumulative error in the recursive chain. Due to the novel learning mechanism of DCESN, numerical coefficients of temporal dependence are presented to explain the short-term and long-term impacts on future energy consumption. Experimental results in four cases indicate that the proposed DCESN has promising performance on MS and MTS prediction, and temporal dependence can be explained in a visible way. Comparative results of DCESN, sliding-window ESN, and long-short term memory (LSTM) demonstrate that the proposed learning mechanism could prevent error accumulation effectively.

Reversible data hiding in encrypted domain (RDH-ED) has greatly attracted researchers as the original content can be losslessly reconstructed after the embedded data are extracted, while the content owner's privacy remains protected. Most of the existing RDH-ED algorithms are designed for grayscale/color images, which cannot be directly applied to other carriers, such as three-dimensional (3D) meshes. With the rapid development of 3D related applications, 3D models have been widely used on the Internet, which motivated us to design a reliable RDH-ED scheme for 3D meshes. The proposed method maps decimals of the vertex coordinates into integers first, so that a bit-stream encryption technique can be executed. With a data-hiding key, several least-significant bits are operated to embed data. By using the encryption key, a receiver can roughly reconstruct the content of the mesh. According to the data-hiding key, with the aid of spatial correlation in natural mesh models, the embedded data can be successfully extracted and the original mesh can be perfectly recovered. Experiments show that the proposed method has a high data-embedding payload, maintains high values of the decrypted meshes, and has low computational complexity.