Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing

Vehicular edge computing (VEC) is a promising technology to support real-time applications through caching the contents in the roadside units (RSUs), thus vehicles can fetch the contents requested by vehicular users (VUs) from the RSU within short time. The capacity of the RSU is limited and the contents requested by VUs change frequently due to the high-mobility characteristics of vehicles, thus it is essential to predict the most popular contents and cache them in the RSU in advance. The RSU can train model based on the VUs' data to effectively predict the popular contents. However, VUs are often reluctant to share their data with others due to the personal privacy. Federated learning (FL) allows each vehicle to train the local model based on VUs' data, and upload the local model to the RSU instead of data to update the global model, and thus VUs' privacy information can be protected. The traditional synchronous FL must wait all vehicles to complete training and upload their local models for global model updating, which would cause a long time to train global model. The asynchronous FL updates the global model in time once a vehicle's local model is received. However, the vehicles with different staying time have different impacts to achieve the accurate global model. In this paper, we consider the vehicle mobility and propose an Asynchronous FL based Mobility-aware Edge Caching (AFMC) scheme to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. Experimental results show that AFMC outperforms other baseline caching schemes.Comment: This paper has been submitted to The 14th International Conference on Wireless Communications and Signal Processing (WCSP 2022

Enabling Bus Transit Service Quality Co-Monitoring Through Smartphone-Based Platform

The growing ubiquity of smartphones offers public transit agencies an opportunity to transform ways to measure, monitor, and manage service performance. The potential of a new tool is demonstrated for engaging customers in measuring satisfaction and co-monitoring [Editor’s note: This is the authors’ word, meaning “agencies using public feedback to supplement official monitoring and regulation.”] bus service quality. The pilot project adapted a smartphone-based travel survey system, Future Mobility Sensing, to collect real-time customer feedback and objective operational measurements on specific bus trips. The system used a combination of GPS, Wi-Fi, Bluetooth, and accelerometer data to track transit trips while soliciting users’ feedback on trip experience. Though not necessarily intended to replace traditional monitoring channels and processes, these data can complement official performance monitoring through a more real-time, customer-centric perspective. The pilot project operated publicly for 3 months on the Silver Line bus rapid transit in Boston, Massachusetts. Seventy-six participants completed the entrance survey; half of them actively participated and completed more than 500 questionnaires while on board either at the end of a trip, at the end of a day, or both. Participation was biased toward frequent Silver Line users, the majority of whom were white and of higher income. Indicative models of user-reported satisfaction reveal some interesting relationships, but the models can be improved by fusing the app-collected data with actual performance characteristics. Broader and more sustained user engagement remains a critical future challenge

Hydro-Volcanism in the Longgang Volcanic Field, Northeast China: Insights from Topography, Stratigraphy, Granulometry and Microtexture of Xidadianzi Maar Volcano

Hydro-volcanism in the Longgang volcanic field (LVF) of Northeast China has produced a dozen maars with features of complex sequences. To better understand the formation mechanism of maar volcanos in the LVF, this study focuses on the Xidadianzi (XDDZ) maar volcano, located in the Jinchuan valley of the LVF. Based on detailed stratigraphy analysis, 14C geochronology, grain-size distribution, and scanning electron microscopy (SEM) analysis, the eruptive sequence of the XDDZ volcano, including the South Crater and the North Crater, was constructed. The whole sequence was formed after four eruptive phases, including a wet phreatomagmatic eruption, an explosive magmatic eruption, a dry and hot phreatomagmatic eruption, and a small explosive magmatic eruption. 14C geochronology indicates that the formation age of XDDZ is 15,900 ± 70 years, BP. Topographic and stratigraphic characteristics show that the landforms of two craters were damaged and buried because of the destruction of lava flows and agricultural modification. The NE- trending fissure in the hard rock area is thought to participate in the formation of the XDDZ maar volcano

Design and Construction Method of High-low Span Cross Support System for Frame Structures

Taking a high-rise frame-shear wall structure project in Jilin Province as an example, combined with current specifications, standards, and regulations, this paper proposes the design and construction method of the scaffold support system for the high-low span and high formwork between the basement and the main structure；In the construction, the side formwork of the high-low span beam adopts the method of suspending formwork, and the combination of fixing support and drawing bolts on both sides. The floor slab is poured first, followed by the high-low span, and the pouring is carried out in layers and sections, ensuring the quality of the project and shortening the construction period. After construction verification, the various acceptance values are better than the current engineering acceptance evaluation standard values, and it is safe and reliable. This provides a reference for the design and construction of similar high-low span and high-formwork projects in frame structures

Anchor-Free Object Detection with Scale-Aware Networks for Autonomous Driving

Current anchor-free object detectors do not rely on anchors and obtain comparable accuracy with anchor-based detectors. However, anchor-free object detectors that adopt a single-level feature map and lack a feature pyramid network (FPN) prior information about an object’s scale; thus, they insufficiently adapt to large object scale variation, especially for autonomous driving in complex road scenes. To address this problem, we propose a divide-and-conquer solution and attempt to introduce some prior information about object scale variation into the model when maintaining a streamlined network structure. Specifically, for small-scale objects, we add some dense layer jump connections between the shallow high-resolution feature layers and the deep high-semantic feature layers. For large-scale objects, dilated convolution is used as an ingredient to cover the features of large-scale objects. Based on this, a scale adaptation module is proposed. In this module, different dilated convolution expansion rates are utilized to change the network’s receptive field size, which can adapt to changes from small-scale to large-scale. The experimental results show that the proposed model has better detection performance with different object scales than existing detectors