Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper proposes an approach to discovering interdisciplinary research automatically. The method generates an IRD-BERT neural network model for discovering interdisciplinary research based on the pre-trained model BERT. IRD-BERT is used to simulate the domain knowledge of experts, and author keywords can be projected into vector space by this model. According to the keyword distribution in the vector space, keywords with semantic anomalies can be identified. Papers that use these author keywords are likely to be interdisciplinary research. This method is applied to discover interdisciplinary research in the deep learning research field, and its performance is better than that of similar methods.
Replication is widely used in cloud storage systems to provide availability and reliability for outsourced cloud data. However, untrusted providers would like to maintain fewer replicas than promise for economic profits or other benefits. Since providers hardly ever offer remote checking tools, it becomes a challenge to validate from user side whether Providers are faithfully keeping enough replicas or not. Existing solutions have several disadvantages, including low efficiency, high computation overload, and lack of supporting for colluding attack. In this article, a collector‐based multiple replica storage model is introduced. Based on that, a novel multiple replica possession proving scheme public key segment is proposed in detail. Particularly, a public key is divided into several private segments, which will be distributed to corresponding storage servers for evidence generation. Only if every replica node shows its correct possession evidence, can the challenge be concluded as success. Theoretical analysis and prototype experiments show the correctness, security, and efficiency of the scheme.
Cloud storage services bring great convenience to users, but also make data owners lose direct control of their data. How to ensure that deleted cloud data can never be recovered by cloud servers or attackers is a key issue in the field of cloud storage security, which is important to protect user privacy and data confidentiality in the cloud environment. Most existing schemes have the drawbacks of overreliance on key destruction and having the ability to decrypt part of the ciphertext after cracking the key. To solve these problems, a novel cloud data assured deletion scheme based on strong nonseparability is presented. By combining XOR operation with the block cipher, the cipher data become strongly nonseparable; thus, destroying any piece of cipher data will result in unrecoverable original data. Theoretical analysis and experimental results both show that the scheme achieves the expected goals of assured deletion, which has obvious performance advantages and stronger security compared with similar schemes.
Abstract. As a new financial model,"Internet Finance +PPP" is popular because of its low cost, high efficiency characteristics. PPP model works on introducing social capital into the infrastructure investment .The combination of the two models will form a new pattern of economic cooperation. And "Internet Finance +PPP" model will become an important financial means of infrastructure projects. However, since this new pattern is still on the exploration stage, there will be a variety of risks in their practical application. This paper mainly analyzes the main risks of this model, and proposes the corresponding control measures.
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