区块链(Blockchain)是由节点参与的分布式数据库系统,它的特点是不可更改,不可伪造,也可以将其理解为账簿系统(ledger)。它是比特币的一个重要概念,完整比特币区块链的副本,记录了其代币(token)的每一笔交易。通过这些信息,我们可以找到每一个地址,在历史上任何一点所拥有的价值。

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2019年12月23日,百度发布了对2020年10大科学技术趋势的预测,在过去的一年中,一些新兴技术的集成加速。人工智能的发展门槛也有所降低,而工业智能导致了第四次工业革命的到来。2019年把这些种子深埋在科技的土壤里,便可以展望2020年未来蓝图,科技仍然是社会的重要功能,也是未来的关键。

十大趋势包括:

  • 趋势1:人工智能技术将实现大规模生产的工业化。到2020年,将会有很多“人工智能工厂”
  • 趋势2:2020年将是AI芯片大规模实施的关键一年
  • 趋势3:深度学习技术将渗透到整个行业并得到大规模应用
  • 趋势4:AutoML,自动机器学习,将大大降低机器学习的门槛
  • 趋势5:多模式深度语义理解将变得更加成熟并被广泛使用
  • 趋势6:自然语言处理技术将与知识深度融合,并且将广泛使用用于一般自然语言理解的计算平台
  • 趋势7:物联网(IoT)将在三个方向突破:边界,维度和场景
  • 趋势8:智能交通将加快其在公园和城市等各种场景中的实施
  • 趋势9:区块链技术将以更加务实的方式集成到更多场景中
  • 趋势10:量子计算将迎来新一轮的爆炸式增长,为AI和云计算注入新的活力
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Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.

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