In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.

Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.

With the fast proliferation of device sensing and computing, crowed sensing has become the building block of the Internet of things. Consequently, various data collection and incentive mechanisms are investigated for people-centric services. In this paper, we have investigated the problem of privacy-aware people-centric IoT service based on a tailored auction approach. We applied a bi-tier differential privacy methodology on the data collected from crowdsensing IoT devices. A corresponding pricing scheme is also proposed to ensure the property of incentive compatibility, precise service data, and anonymized query results. Comparing to traditional privacy-aware auction schemes which only focus on the cost, our corresponding precise privacy-aware auction scheme provides a tailored IoT service based on the customers' request. The proposed trial query technique is able to provide a precise assessment of service quality, thus improves the efficiency of the people-centric IoT service. The customer could enjoy the convenience of service evaluation before making a bid, while the actual service data is anonymized to guarantee the service providers' interests. We evaluate the proposed bi-tier differential privacy schema for auction-based service by conducting extensive simulations. The experimental results show that our proposed method yields higher data utility and accuracy for the IoT service customers with privacy concerns.

Accurate cloud detection is a crucial initial stage in optical satellite remote sensing. In this study, a daytime cloud mask model is proposed for the Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun 4A (FY-4A) satellite based on a deep learning approach. The model, named “Convolutional and Attention-based Cloud Mask Net (CACM-Net)”, was trained using the 2021 dataset with CALIPSO data as the truth value. Two CACM-Net models were trained based on a satellite zenith angle (SZA) < 70° and >70°, respectively. The study evaluated the National Satellite Meteorological Center (NSMC) cloud mask product and compared it with the method established in this paper. The results indicate that CACM-Net outperforms the NSMC cloud mask product overall. Specifically, in the SZA < 70° subset, CACM-Net enhances accuracy, precision, and F1 score by 4.8%, 7.3%, and 3.6%, respectively, while reducing the false alarm rate (FAR) by approximately 7.3%. In the SZA > 70° section, improvements of 12.2%, 19.5%, and 8% in accuracy, precision, and F1 score, respectively, were observed, with a 19.5% reduction in FAR compared to NSMC. An independent validation dataset for January–June 2023 further validates the performance of CACM-Net. The results show improvements of 3.5%, 2.2%, and 2.8% in accuracy, precision, and F1 scores for SZA < 70° and 7.8%, 11.3%, and 4.8% for SZA > 70°, respectively, along with reductions in FAR. Cross-comparison with other satellite cloud mask products reveals high levels of agreement, with 88.6% and 86.3% matching results with the MODIS and Himawari-9 products, respectively. These results confirm the reliability of the CACM-Net cloud mask model, which can produce stable and high-quality FY-4A AGRI cloud mask results.

As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput.

Cloud optical thickness (COT) stands as a critical parameter governing the radiative properties of clouds. This study develops a convolutional neural network (CNN) model to retrieve the COT of single-layer non-precipitating clouds during nighttime using FY-4B satellite data. The model integrates multi-channel brightness, temperature, and geographic and temporal features, without relying on auxiliary meteorological data, using the multi-point averaged 532 nm COT from CALIPSO as ground truth for training. Performance evaluation demonstrates robust retrieval accuracy, achieving coefficients of determination (R²) of 0.88 and 0.73 for satellite zenith angles (SAZAs) < 70° and >70°, respectively. Key advancements include the incorporation of temporal features, the Squeeze-and-Excitation (SE) module, and a multi-point averaging technique, each validated through ablation experiments to reduce bias and enhance stability. Meanwhile, a model error analysis experiment was conducted that further clarified the performance boundaries of the model. These findings underscore the model’s capability to retrieve the COT of single-layer non-precipitating clouds during nighttime with high precision.