Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: There is provided a computer implemented method for the automated analysis or use of data, comprising the steps of: (a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language; in which the structured, machine-readable representation of data includes a plurality of tenets defining rules, the plurality of tenets including at least one constraint tenet, the at least one constraint tenet preventing a bad action; wherein the plurality of tenets comprise different sets of tenets; (b) using the different sets of tenets in different contexts, e.g. for different types of chatbots, or for different end-users.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: There is provided a computer implemented method for translating between a first natural language and a second natural language, comprising the steps of: (a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language; (b) receiving a word or sequence of words in the first natural language to be translated into the second natural language; (c) automatically translating that word or sequence of words expressed in the first natural language into the second natural language by first translating that word or sequence of words expressed in the first natural language into the machine readable language, and then translating from the machine readable language into the second natural language.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of:(a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: There is provided a computer implemented method for the automated analysis or use of data, comprising the steps of: (a) storing in a non-transitory computer-readable medium a structured, machine-readable representation of data that conforms to a machine-readable language; in which the structured, machine-readable representation of data includes a plurality of tenets defining rules, the plurality of tenets including at least one constraint tenet, the at least one constraint tenet preventing a bad action; wherein the plurality of tenets comprise different sets of tenets; (b) using the different sets of tenets in different contexts, e.g. for different types of chatbots, or for different end-users.

Abstract: A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

Abstract: Disclosed is a public random number generation method based on a blockchain, including: selecting a node group G containing N trusted nodes; determining a first time point tm and a second time point tn respectively for generating an m-th/n-th block, wherein the former is earlier, and the m-th block is fixed and cannot be tampered at tn; at the first time point tm, enabling each of the N trusted nodes to separately generate a sub-random number rj as a component forming a random number X, wherein j=1, 2, . . . , N, on which delayed encryption is performed, with corresponding results placed in the m-th block; and at the second time point tn, decrypting the delayed encryption results to obtain decrypted data of them all that are all of sub-random number rj, on which operation is performed to obtain the random number X as a final available public random number.

Abstract: First and second devices store respective device data and private keys. The first-device data is additionally stored by the second device and by a proxy; and the second-device data is additionally stored by the first device and by the proxy. In a commitment phase, each of the first and second devices uses its respective device data, private key and a random nonce to generate a respective one-time first-device or second-device commitment value, which it sends to the proxy. In a checking phase, the devices communicate secret-key information to the proxy, which verifies the received one-time commitment values. In a digest phase, the proxy calculates a one-time digest, which it sends to the second device. The second device then verifies the received one-time digest to authenticate the first device.

Abstract: A method of operating a trusted execution environment for analysing source code from a third-party vendor is disclosed. The method comprises receiving an encrypted source code and using a secure key stored within the trusted execution environment to decrypt the encrypted source code to produce a decrypted source code. The decrypted source code is analyzed to determine a trust metric for the decrypted source code. The decrypted source code is disallowed from being communicated to an external entity outside of the trusted execution environment.

Abstract: Disclosed is a method and device for preventing blockchain forking. The method includes: selecting s consecutive blocks Bn to Bn+s; generating a key pair for block Bm by a node An that creates the block Bn; holding the node An active in the blockchain's continuously generating blocks Bn+1 to Bn+s until the block Bn+s of s consecutive blocks becomes tamper-proof; in response to that, signing on the block Ban+s with the private key PK?n; in response to the blockchain's subsequently generating a block Bm(m>n+s), placing the signature in Bm; making nodes creating each of the s blocks Bn to Bn+s all execute afore-mentioned steps, thereby forming multiple backlinks associated with the blockchain's block size. The number of backlinks is used for determining blockchain forking by a newly-added node creating a new block.

Abstract: The method of identity authentication at the user is provided to prove to the certificate authority that a key is owned by a user. The method can comprise selecting a certain number of keys from a set of keys of the user, obtaining a hash value of a correspondence between each key in the certain number of keys and a user identifier of the user respectively, and transmitting the obtained hash values to the certificate authority, and after receiving from the certificate authority a notification regarding a first subset of hash values, sending keys corresponding to the first subset of hash values as a first subset of keys to the certificate authority. The first subset of hash values can be selected by the certificate authority from the obtained hash values. Zero know ledge proof can be achieved with this technical solution.

Abstract: Disclosed are methods and apparatuses for electronic signature. The method for electronic signature comprises obtaining a hash value of a first key created for a user and a user identifier of the user, generating a key certificate of the first key based on the obtained hash value, the user identifier and a current key, recording the key certificate on a public medium, which public medium ensures that information published thereon is not tampered with, signing a file with the first key and recording a resulting file signature and the file on the public medium, and recording the first key on the public medium only after the file is already on the public medium. With the technical solution of the disclosure, a key can be effectively utilized.

Abstract: Disclosed are methods and systems for identity authentication, and a computing device and a storage medium. The method of identity authentication at the user is provided to prove to the certificate authority that a key is owned by a user. The method can comprise selecting a certain number of keys from a set of keys of the user, obtaining a hash value of a correspondence between each key in the certain number of keys and a user identifier of the user respectively, and transmitting the obtained hash values to the certificate authority, and after receiving, from the certificate authority a notification regarding a first subset of hash values, sending keys corresponding to the first subset of hash values as a first subset of keys to the certificate authority. The first subset of hash values can be selected by the certificate authority from the obtained hash values. Zero knowledge proof can be achieved with the technical solution of the disclosure.

Abstract: A computer implemented method of exchanging first valuable data at a first node for second valuable data from a second node, the method comprising the steps of: applying a first encryption to a first plurality of messages, at the first node, with a function having a commutative property, so as to create a blinded first plurality of messages; sending the blinded first plurality of messages from the first node to the second node, wherein the first valuable data is concealed in one message of the blinded first plurality of messages; receiving a blinded second plurality of messages at the first node, wherein the second valuable data is concealed in one message of the blinded second plurality of messages and the blinded second plurality of messages have been encrypted with a second encryption; in response to receiving the blinded second plurality of messages at the first node, applying a third encryption to the blinded second plurality of messages with a function having a commutative property so as to create do

Abstract: First and second devices store respective device data and private keys. The first-device data is additionally stored by the second device and by a proxy; and the second-device data is additionally stored by the first device and by the proxy. In a commitment phase, each of the first and second devices uses its respective device data, private key and a random nonce to generate a one-time first-device commitment value, which it sends to the proxy. In a checking phase, the devices communicate secret-key information to the proxy, which verifies the received one-time commitment values. In a digest phase, the proxy calculates a one-time digest, which it sends to the second device. The second device (101) then verifies the received one-time digest to authenticate the first device.