Real-time collaborative programming is an emerging technology that supports a team of programmers to concurrently view and edit source code documents, with the benefits of enhancing team productivity and reducing project cost. Shared terminal is one crucial component of real-time collaborative programming environments, which facilitates interactive and instant peer support in debugging scenarios. In this study, we propose a novel approach named FlexSecure to address two major challenges in existing shared terminals. FlexSecure supports unconstrained and flexible shared terminal sessions that allow any collaborator to initiate, and meanwhile, preserves the local security of the initiator by incorporating fine-grained permission control to prevent risky command execution. Prototype implementation has validated the feasibility of FlexSecure, and user evaluation has demonstrated its effectiveness and satisfactory performance.

With the advancement of autonomous driving technology, trajectory prediction has become a critical task for ensuring traffic safety and intelligent decision-making. Existing motion forecasting models suffer from HD (High-Definition) map dependency, leading to high costs and poor adaptability. Furthermore, their accuracy sharply declines when maps are unavailable, motivating research into map-free alternatives. However, map-free models typically exhibit lower accuracy. To address this issue, we propose a universal enhancement framework that employs trajectory-map contrastive learning, utilizing a trajectory-to-map encoder to extract implicit map representations from raw trajectories, thereby improving performance. Extensive experiments on the Argoverse dataset demonstrate that, after incorporating our trajectory-to-map encoder into map-free models, the average minADE and minFDE are improved by 2.7% and 3.5%, respectively. These results underscore our method’s robustness and generalizability in enhancing map-free models, confirming the efficacy of implicit map representation learning and offering a promising solution for HD-map-free autonomous driving in dynamic open-road environments.

Ensuring the resilience of deep learning algorithms against adversarial attacks during edge-cloud data transmission between edge and cloud systems is crucial. Although significant strides have been made in enhancing the accuracy of hazardous driving scenario detection in autonomous vehicles, bolstering the robustness against adversarial attacks during data transfer and model recognition remains an urgent challenge. In this paper, we propose a novel approach to enhance adversarial robustness and maintain high accuracy in benign data. Using a lightweight CNN model, we detect hazardous driving scenarios, while a generative adversarial network generates decision boundary samples from original scenario images. These samples are incorporated into adversarial training, preventing overfitting on adversarial examples. Experimental results show our approach achieves robust accuracy (AUC) of 0.83 and 0.66 under FGSM and PGD attacks, surpassing vanilla adversarial training and TRADES methods with improving the standard accuracy (AUC) on benign samples by 0.22 and 0.12 relatively. Our proposed method advances beyond current adversarial training techniques to significantly enhance the model’s resilience to adversarial attacks during edge-cloud data transmission phases and minimize the loss of standard accuracy simultaneously. This advancement is crucial in enhancing the capability to correctly detect a wider range of hazardous driving scenarios, thereby providing significant support for the secure deployment of autonomous vehicles in edge-cloud collaborative environments.

The isoelectric line is an important component that connects the steady arm and the drop bracket of catenary in high-speed railway. The loose strands of isoelectric line can be commonly observed in real-life applications. In this paper, an automatic fault detection system for the loose strands of the isoelectric line is proposed. This system consists of three stages. First, a convolutional neural network is adopted to extract the isoelectric line features. To accurately and quickly learn these features, an improved feature extraction network, called as the isoelectric line network, is presented. Using the images captured from catenary inspection vehicles, the image areas that contain the isoelectric lines are obtained based on the Faster region-based convolutional neural network. Second, the image segmentation is carried out based on the Markov random field model. And, the accurate isoelectric line pixels are obtained from the smallest image area extracted from the first stage. In the final stage, the fault state is given by analyzing the quantity of the independent connection regions and the pixels' standard deviation. Experimental results show that the proposed system has a high detection accuracy. Furthermore, compared with the convolutional neural networks (the Simonyan and Zisserman model and the Zeiler and Fergus model) and a typical detection method (Histogram of Oriented Gradient + Support Vector Machine), the proposed network has better performance for the isoelectric line location.