Understanding changes in teacher beliefs and identity formation: A case study of three novice teachers in Hong Kong

Novice teachers often undergo an identity shift from learner to teacher. Along this process, their instructional beliefs change considerably which in turn affect their teacher identity formation. Drawing on data collected mainly through interviews with three novice English teachers formore than one year, the present study examines their firstyear teaching experience in Hong Kong secondary schools, focusing on changes of their English teaching beliefs and the impact of these changes on their identity construction. Findings reveal that while the teachers’ initial teaching beliefs were largely shaped in their prior school learning and learning-to-teach experience, these beliefs changed and were reshaped a great deal when encountering various contextual realities, and these changes further influenced their views on their teacher identity establishment, unfortunately in a more negative than positive direction. The study sheds light on the importance of institutional support in affording opportunities for novice teachers’ workplace learning and professional development

k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training

For a data holder, such as a hospital or a government entity, who has a privately held collection of personal data, in which the revealing and/or processing of the personal identifiable data is restricted and prohibited by law. Then, "how can we ensure the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification?" becomes a challenge issue. In this work, we propose an approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN, which leverages the k-Same-Anonymity mechanism, the Generative Adversarial Network, and the hyperparameter tuning methods. Moreover, to speed up model training and reduce memory consumption, the mixed precision training technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, to validate its applicability, the proposed work has been applied to actual datasets - RafD and CelebA for performance testing. Besides protecting privacy of high-resolution facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters

Give me a hint: An ID-free small data transmission protocol for dense IoT devices

IoT (Internet of Things) has attracted a lot of attention recently. IoT devices need to report their data or status to base stations at various frequencies. The IoT communications observed by a base station normally exhibit the following characteristics: (1) massively connected, (2) lightly loaded per packet, and (3) periodical or at least mostly predictable. The current design principals of communication networks, when applied to IoT scenarios, however, do not fit well to these requirements. For example, an IPv6 address is 128 bits, which is much longer than a 16-bit temperature report. Also, contending to send a small packet is not cost-effective. In this work, we propose a novel framework, which is slot-based, schedule-oriented, and identity-free for uploading IoT devices' data. We show that it fits very well for IoT applications. The main idea is to bundle time slots with certain hashing functions of device IDs, thus significantly reducing transmission overheads, including device IDs and contention overheads. The framework is applicable from small-scale body-area (wearable) networks to large-scale massively connected IoT networks. Our simulation results verify that this framework is very effective for IoT small data uploading

r-Hint: A message-efficient random access response for mMTC in 5G networks

Massive Machine Type Communication (mMTC) has attracted increasing attention due to the explosive growth of IoT devices. Random Access (RA) for a large number of mMTC devices is especially difficult since the high signaling overhead between User Equipments (UEs) and an eNB may overwhelm the available spectrum resources. To address this issue, we propose “respond by hint” (r-Hint), an ID-free handshaking protocol for contention-based RA in mMTC. The core idea of r-Hint is to avoid sequentially notifying contending UEs of their IDs by broadcasting a hint in the RA Response (RAR). To do so, we exploit the concept of prime factorization and hashing to encode the hint such that UEs can extract their required information accordingly. Our simulation results show that r-Hint reduces the RAR message size by 20%–40%. Such reduction can be translated to around 50% improvement of spectrum efficiency in LTE-M

Multi-Metric AutoRec for High Dimensional and Sparse User Behavior Data Prediction

User behavior data produced during interaction with massive items in the significant data era are generally heterogeneous and sparse, leaving the recommender system (RS) a large diversity of underlying patterns to excavate. Deep neural network-based models have reached the state-of-the-art benchmark of the RS owing to their fitting capabilities. However, prior works mainly focus on designing an intricate architecture with fixed loss function and regulation. These single-metric models provide limited performance when facing heterogeneous and sparse user behavior data. Motivated by this finding, we propose a multi-metric AutoRec (MMA) based on the representative AutoRec. The idea of the proposed MMA is mainly two-fold: 1) apply different $L_p$-norm on loss function and regularization to form different variant models in different metric spaces, and 2) aggregate these variant models. Thus, the proposed MMA enjoys the multi-metric orientation from a set of dispersed metric spaces, achieving a comprehensive representation of user data. Theoretical studies proved that the proposed MMA could attain performance improvement. The extensive experiment on five real-world datasets proves that MMA can outperform seven other state-of-the-art models in predicting unobserved user behavior data.Comment: 6 pages, 4 Table

Boosting Point Clouds Rendering via Radiance Mapping

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid spatial frequency collapse and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data would be released soon