Abstract: Behavioral characteristics of at least a first machine component are monitored. A model that represents machine-to-machine interactions between at least the first machine component and at least a further machine component is generated. Using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions is computed, where the incongruity is predicted based on determining a discordance between an expectation of the system and the behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions.

Abstract: Behavioral characteristics of at least a first machine component are monitored. A model that represents machine-to-machine interactions between at least the first machine component and at least a further machine component is generated. Using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions is computed, where the incongruity is predicted based on determining a discordance between an expectation of the system and the behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions.

Abstract: Behavioral characteristics of at least a first machine component are monitored. A model that represents machine-to-machine interactions between at least the first machine component and at least a further machine component is generated. Using the monitored behavioral characteristics and the generated model, an incongruity of a behavior of at least the first machine component and the machine-to-machine interactions is computed, where the incongruity is predicted based on determining a discordance between an expectation of the system and the behavior and the machine-to-machine interactions, and wherein the predicting is performed without using a previously built normative rule of behavior and machine-to-machine interactions.

Abstract: For controlling the broadcasting of a digital document, the method brings at least three actors into play. First actor is an originator user computer system wherein a file is generated for containing the digital document, digital conditioning attributes corresponding to at least one predetermined event that is liable to affect the data in future use and information that secures data integrity are associated with the data. Second actor is a future user computer system which causes an occurrence of the predetermined event. Third actor is a remote computer system arranged for detecting the event occurrence by storing digital conditioning attributes and information that secures data integrity without knowledge of the digital document.

Abstract: In an electronic communication system, relevance levels of an incoming or outgoing message for presenting it to an interlocutor is measured without having to actually interact with the interlocutor, by a method comprising the steps of extracting from the message, a flow of digital signals pertaining to transmission/reception context features, to content of the message and/or to other interlocutors with the interlocutor; weighting probabilistically the digital signals by means of indicators of relative and interrelated frequencies of occurrences of the same digital signals extracted from previous messages; from the results of the above steps, auto-generating a Bayesian network that allows the interlocutor to obtain a probabilistic prediction on the attractiveness of sent/received signals or messages, or find most probably interested interlocutors for a given information or message, each node of the Bayesian network being associated with a signal.

Abstract: In an electronic communication system, relevance levels of an incoming or outgoing message for presenting it to an interlocutor is measured without having to actually interact with the interlocutor, by a method comprising the steps of extracting from the message, a flow of digital signals pertaining to transmission/reception context features, to content of the message and/or to other interlocutors with the interlocutor; weighting probabilistically the digital signals by means of indicators of relative and interrelated frequencies of occurrences of the same digital signals extracted from previous messages; from the results of the above steps, auto-generating a Bayesian network that allows the interlocutor to obtain a probabilistic prediction on the attractiveness of sent/received signals or messages, or find most probably interested interlocutors for a given information or message, each node of the Bayesian network being associated with a signal.

Abstract: In an electronic communication system, relevance levels of an incoming or outgoing message for presenting it to an interlocutor is measured without having to actually interact with the interlocutor, by a method comprising the steps of extracting from the message, a flow of digital signals pertaining to transmission/reception context features, to content of the message and/or to other interlocutors with the interlocutor; weighting probabilistically the digital signals by means of indicators of relative and interrelated frequencies of occurrences of the same digital signals extracted from previous messages; from the results of the above steps, auto-generating a Bayesian network that allows the interlocutor to obtain a probabilistic prediction on the attractiveness of sent/received signals or messages, or find most probably interested interlocutors for a given information or message, each node of the Bayesian network being associated with a signal.

Abstract: The method for protecting data includes: assigning in the IT system of an author user, digital conditioning attributes of the data, corresponding to at least one predetermined event that is liable to affect the data in future use, attributing in the IT system, information that secures data integrity, setting up in the IT system, an envelope file carrying data, digital conditioning attributes affected to the data and information that secures data integrity, storing in a remote IT system, digital conditioning attributes affected to the data and information that secures data integrity, for each predetermined event related to the data, storing in the remote IT system an identifier of the event and its date, and at each connection, storing predetermined events corresponding to data attributes, in the IT system of the author, so that the IT system keeps track, for each event regarding data, the identifier of the event, the identifier of the user at the origin of the event and its date.

Abstract: In an electronic communication system, relevance levels of an incoming or outgoing message for presenting it to an interlocutor is measured without having to actually interact with the interlocutor, by a method comprising the steps of extracting from the message, a flow of digital signals pertaining to transmission/reception context features, to content of the message and/or to other interlocutors with the interlocutor; weighting probabilistically the digital signals by means of indicators of relative and interrelated frequencies of occurrences of the same digital signals extracted from previous messages; from the results of the above steps, auto-generating a Bayesian network that allows the interlocutor to obtain a probabilistic prediction on the attractiveness of sent/received signals or messages, or find most probably interested interlocutors for a given information or message, each node of the Bayesian network being associated with a signal.