Abstract: A method and an electronic device (100) are disclosed for generating semantic representation of a document to determine data security risk associated with the document. The method includes receiving, by a document semantics controller (160) of the electronic device (100), a document in an electronic form and determining, by the document semantics controller (160), raw text. Further, the method includes generating, by the document semantics controller (160), a plurality of sentence blocks using the raw text and determining, by the document semantics controller (160), embeddings for the plurality of sentence blocks. Further, the method includes determining, by the document semantics controller (160), the semantic representation of the document based on the embeddings for each of the sentence blocks; and generating, by the document semantics controller (160), the semantic representation of the document to determine the data security risk associated with the document.

Abstract: A method and an electronic device (100) are disclosed for generating semantic representation of a document to determine data security risk associated with the document. The method includes receiving, by a document semantics controller (160) of the electronic device (100), a document in an electronic form and determining, by the document semantics controller (160), raw text. Further, the method includes generating, by the document semantics controller (160), a plurality of sentence blocks using the raw text and determining, by the document semantics controller (160), embeddings for the plurality of sentence blocks. Further, the method includes determining, by the document semantics controller (160), the semantic representation of the document based on the embeddings for each of the sentence blocks; and generating, by the document semantics controller (160), the semantic representation of the document to determine the data security risk associated with the document.

Abstract: A method includes converting time-series data from a plurality of prognostic and health monitoring (PHM) sensors into frequency domain data. One or more portions of the frequency domain data are labeled as indicative of one or more target modes to form labeled target data. A model including a deep neural network is applied to the labeled target data. A result of applying the model is classified as one or more discretized PHM training indicators associated with the one or more target modes. The one or more discretized PHM training indicators are output.

Abstract: A method and an electronic device (100) are disclosed for generating semantic representation of a document to determine data security risk associated with the document. The method includes receiving, by a document semantics controller (160) of the electronic device (100), a document in an electronic form and determining, by the document semantics controller (160), raw text. Further, the method includes generating, by the document semantics controller (160), a plurality of sentence blocks using the raw text and determining, by the document semantics controller (160), embeddings for the plurality of sentence blocks. Further, the method includes determining, by the document semantics controller (160), the semantic representation of the document based on the embeddings for each of the sentence blocks; and generating, by the document semantics controller (160), the semantic representation of the document to determine the data security risk associated with the document.

Abstract: A security appliance with one or more processors and a memory communicatively coupled to the one or more processors is described. The memory includes behavior profiling service logic that, when executed by the one or more processors, (i) creates a behavior profile for a particular entity based on received incoming data, and (ii) determines whether the behavior profile identifies that a malicious attack is being performed by the particular entity based on a comparison of the behavior profile to a reference profile. The reference profile represents historical behavior of the particular entity that is monitored over a prescribed period of time.

Abstract: A method includes converting time-series data from a plurality of prognostic and health monitoring (PHM) sensors into frequency domain data. One or more portions of the frequency domain data are labeled as indicative of one or more target modes to form labeled target data. A model including a deep neural network is applied to the labeled target data. A result of applying the model is classified as one or more discretized PHM training indicators associated with the one or more target modes. The one or more discretized PHM training indicators are output.

Abstract: A system and method for providing health indication of a mechanical system, includes receiving signals indicative of vibration data of the mechanical system; pre-training features in the signals with a model; determining information related to vibration signatures in the signals; associating the vibration signatures with historical vibration data of the mechanical system; and building a multi-layer Deep Neural Network (DNN) from the vibration signatures and the historical vibration data.

Abstract: Embodiments are directed to obtaining an impact energy signal associated with each of a plurality of teeth of a gear over a revolution of a shaft associated with the gear, generating, by a computing device comprising a processor, a profile of the impact energy signal, and declaring a fault associated with an identified tooth included in the plurality of teeth based on an analysis of the profile.

Abstract: Embodiments are directed to obtaining an impact energy signal associated with each of a plurality of teeth of a gear over a revolution of a shaft associated with the gear, generating, by a computing device comprising a processor, a profile of the impact energy signal, and declaring a fault associated with an identified tooth included in the plurality of teeth based on an analysis of the profile.

Abstract: Our invention describes a method and a system for matching securely an unknown audio recording with known audio recordings. A plurality of known audio recordings, each known audio recording associated with an index to information uniquely identifying the known audio recording is stored on a server. An unknown audio recording cross-correlated securely with each of the plurality of known audio recordings to determine a best matching known audio recording, in which the unknown audio recording and the plurality of known audio recordings are encrypted with a public key. A best matching known audio recording is determined securely according to the cross-correlation. Next, the index of the best matching known audio recording is determined securely. Finally, the information associated with the index of the best matching known audio recording is provided securely to a user of the unknown recording.

Abstract: A system and method recognizes speech securely using a secure multi-party computation protocol. The system includes a client and a server. The client is configured to provide securely speech in a form of an observation sequence of symbols, and the server is configured to provide securely a multiple trained hidden Markov models (HMMs), each trained HMM including a multiple states, a state transition probability distribution and an initial state distribution, and each state including a subset of the observation symbols and an observation symbol probability distribution. The observation symbol probability distributions are modeled by mixtures of Gaussian distributions. Also included are means for determining securely, for each HMM, a likelihood the observation sequence is produced by the states of the HMM, and means for determining a particular symbol with a maximum likelihood of a particular subset of the symbols corresponding to the speech.

Abstract: Our invention describes a method and a system for matching securely an unknown audio recording with known audio recordings. A plurality of known audio recordings, each known audio recording associated with an index to information uniquely identifying the known audio recording is stored on a server. An unknown audio recording cross-correlated securely with each of the plurality of known audio recordings to determine a best matching known audio recording, in which the unknown audio recording and the plurality of known audio recordings are encrypted with a public key. A best matching known audio recording is determined securely according to the cross-correlation. Next, the index of the best matching known audio recording is determined securely. Finally, the information associated with the index of the best matching known audio recording is provided securely to a user of the unknown recording.

Abstract: A system and method recognizes speech securely. The system includes a client and a server, The client is configured to provide securely speech in a form of an observation sequence of symbols, and the server is configured to provide securely a multiple trained hidden Markov models (HMMs), each trained HMM including a multiple states, a state transition probability distribution and an initial state distribution, and each state including a subset of the observation symbols and an observation symbol probability distribution. The observation symbol probability distributions are modeled by mixtures of Gaussian distributions. Also included are means for determining securely, for each HMM, a likelihood the observation sequence is produced by the states of the HMM, and means for determining a particular symbol with a maximum likelihood of a particular subset of the symbols corresponding to the speech.