With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as ${R} ^{2}$ , mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and ${R} ^{2}$ are 16.1, 0.79, and 0.35 at the Gucheng station and 19.53, 0.82, and 0.36 at the Dongsi station.

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent efficiency, to achieve the following two pieces of work: (1) Integrated local differential privacy paradigm into DS-ADMM to provide the privacy-preserving property; (2) Introduced a stochastic quantized function to reduce transmission overheads in ADMM to further improve efficiency. We named our work DS-ADMM++, in which one ’+’ refers to differential privacy, and the other ’+’ refers to quantized techniques. DS-ADMM++ is the first to perform efficient and private matrix factorization under the scenarios of differential privacy and DS-ADMM. We conducted experiments with benchmark data sets to demonstrate that our approach provides differential privacy and excellent scalability with a decent loss of accuracy.

Human pose estimation (HPE) is a crucial computer vision task with a wide range of applications in sports medicine, healthcare, virtual reality, and human-computer interaction. The demand for real-time HPE solutions necessitates the development of efficient deep-learning models that can be deployed on resource-constrained devices. While a few surveys exist in this area, none delve deeply into the critical intersection of efficiency and performance. This survey reviews the state-of-the-art efficient deep learning approaches for real-time HPE, focusing on strategies for improving efficiency without compromising accuracy. We discuss popular backbone networks for HPE, model compression techniques, network pruning and quantization, knowledge distillation, and neural architecture search methods. Furthermore, we critically analyze the existing works, highlighting their strengths, weaknesses, and applicability to different scenarios. We also present an overview of the evaluation datasets, metrics, and design for efficient HPE. Finally, we identify research gaps and challenges in the field, providing insights and recommendations for future research directions in developing efficient and scalable HPE solutions.

This paper introduces a novel approach to improving the accuracy of satellite orbit prediction (OP) by combining XGBoost with the extended Kalman filter (EKF). While EKF is a well-established method in orbit determination, it falls short when dealing with OP due to the absence of observation data during the prediction period. To our knowledge, this paper is the first attempt to apply XGBoost, a leading ensemble learning method, to address this challenge. Furthermore, we adopt the orbit-separate strategy rather than the orbit-mixed strategy for prediction. This choice is based on the belief that the former outperforms the latter, given that it treats each orbit uniquely. This belief aligns with the understanding that each orbit follows its own physical laws, influenced by distinct perturbations such as gravity, atmospheric drag, radiation pressure, and the gravitational pull of the Sun and Moon. The experimental results, conducted on five Starlink satellites, highlight the effectiveness of the XGBoost-based ensemble learning method when modeled with the orbit-separate strategy. It successfully captures the differences between predicted values from the physical model and true values simulated by the EKF. Notably, when compared to the state-of-the-art model employing an orbit-mixed strategy, the XGBoost model with an orbit-separate strategy demonstrates an overall improvement in prediction accuracy.

Earth observation (EO) data provenance is vital for facilitating data sharing and cooperative processing. However, existing techniques for managing EO data provenance still have various weaknesses, including decentralization, traceability, transparency, tamper-proofing, and security protection. Despite being a transformative solution in various domains, the potential of blockchain technology in EO data provenance remains largely unexplored. This article introduces a blockchain-based solution for EO data provenance, aiming to facilitate data sharing and traceability. We have implemented a prototype based on the blockchain technology and conducted a performance evaluation. To the best of authors' knowledge, this is the first paper to explore the application of blockchain in the management of EO data provenance.