In the rapidly evolving landscape of autonomous vehicles and advanced navigation systems, the accuracy of high-definition maps and real-time updating has become paramount. However, in this progression, the security of map data has not received adequate attention, although the accuracy of the data can be easily altered when the system is breached. Thus, this paper introduces a novel approach to democratizing the process of high-definition map updates by leveraging consortium blockchain technology specifically designed for Proof of Presence and Reputation (POP-R) to safeguard the update process. Our proposed system leverages the presence and reputation of vehicles through infrastructure nodes to enhance the accuracy and reliability of HD map updates. We created a trusted ecosystem for maintaining high-definition maps, marked by a superior safety score across three scenarios compared to the standard proof of reputation technique. Additionally, it demonstrates high efficiency, achieving 12,000 transactions per second (TPS) for data queries and more than 2,500 TPS for data writing in our blockchain network. This efficiency proved our prowess in the lightweight computational power required, suitable for decentralized and crowdsourced-based systems. Through our POP-R framework, we lay the foundation for a new decentralized approach to the evolution of high-definition maps in the era of autonomous mobility.

Adequacy of citations is very important for a scientific paper. However, it is not an easy job to find appropriate citations for a given context, especially for citations in different languages. In this paper, we define a novel task of cross-language context-aware citation recommendation, which aims at recommending English citations for a given context of the place where a citation is made in a Chinese paper. This task is very challenging because the contexts and citations are written in different languages and there exists a language gap when matching them. To tackle this problem, we propose the bilingual context-citation embedding algorithm (i.e. BLSRec-I), which can learn a low-dimensional joint embedding space for both contexts and citations. Moreover, two advanced algorithms named BLSRec-II and BLSRec-III are proposed by enhancing BLSRec-I with translation results and abstract information, respectively. We evaluate the proposed methods based on a real dataset that contains Chinese contexts and English citations. The results demonstrate that our proposed algorithms can outperform a few baselines and the BLSRec-II and BLSRec-III methods can outperform the BLSRec-I method.