Abstract: A user device may invoke, for a user associated with an unavailable user device, a guest mode, and may connect the user device with a network device based on invoking the guest mode. The user device may provide credentials of the user and a secure input of the user to the network device based on invoking the guest mode, and may receive an identity service and an emergency service for the user when the secure input is authenticated by the network device. The user device may associate, via the identity service, the user with the user device to enable the user to utilize the emergency service, and may provide, via the emergency service, one or more emergency notifications. The user device may receive an indication of the user exiting the guest mode, and may remove the credentials of the user from a memory based on the indication.

Abstract: A user device may invoke, for a user associated with an unavailable user device, a guest mode, and may connect the user device with a network device based on invoking the guest mode. The user device may provide credentials of the user and a secure input of the user to the network device based on invoking the guest mode, and may receive an identity service and an emergency service for the user when the secure input is authenticated by the network device. The user device may associate, via the identity service, the user with the user device to enable the user to utilize the emergency service, and may provide, via the emergency service, one or more emergency notifications. The user device may receive an indication of the user exiting the guest mode, and may remove the credentials of the user from a memory based on the indication.

Abstract: A user device may invoke, for a user associated with an unavailable user device, a guest mode, and may connect the user device with a network device based on invoking the guest mode. The user device may provide credentials of the user and a secure input of the user to the network device based on invoking the guest mode, and may receive an identity service and an emergency service for the user when the secure input is authenticated by the network device. The user device may associate, via the identity service, the user with the user device to enable the user to utilize the emergency service, and may provide, via the emergency service, one or more emergency notifications. The user device may receive an indication of the user exiting the guest mode, and may remove the credentials of the user from a memory based on the indication.

Abstract: A user device may invoke, for a user associated with an unavailable user device, a guest mode, and may connect the user device with a network device based on invoking the guest mode. The user device may provide credentials of the user and a secure input of the user to the network device based on invoking the guest mode, and may receive an identity service and an emergency service for the user when the secure input is authenticated by the network device. The user device may associate, via the identity service, the user with the user device to enable the user to utilize the emergency service, and may provide, via the emergency service, one or more emergency notifications. The user device may receive an indication of the user exiting the guest mode, and may remove the credentials of the user from a memory based on the indication.

Abstract: Briefly, in accordance with at least one embodiment, a method or apparatus capable of creating or generating web pages dynamically (or a portion thereof) is disclosed.

Abstract: An error bound that indicates how well a hypothesis function approximates an unknown target function over a set of out-of-sample examples is computed from known error bounds for basis functions, as follows. An optimization problem is formed in which basis function error bounds imply constraints on feasible outputs of the target function over out-of-sample inputs. The optimization problem is solved to find an upper bound on the differences between the hypothesis function outputs and feasible target function outputs. This upper bound is a an error bound for the hypothesis function.

Abstract: The present invention discloses a technique for extracting data from a file. In accordance with the present invention, a request to extract one or more data records from the file is received. The data records within the file are identified, without using prior knowledge of a structure of the file. The data records are then extracted.

Abstract: Given a set of basis functions, a set of example inputs, and a set of uniform error bounds for the basis functions over the example inputs, a quadratic program is formed. The quadratic program is solved, producing a solution vector and a solution value. A hypothesis function is formed through fusion by using the solution vector to weight the outputs of the basis function. The hypothesis function is a function with minimum error bound among the functions formed by convex combination of basis function outputs. The solution value is an error bound for the hypothesis function. The error bound is logically implied by the uniform error bounds over the basis functions rather than uniform error bounds over the entire class of functions formed by convex combination of basis function outputs.

Abstract: The present invention discloses a technique for extracting data from a file. In accordance with the present invention, a request to extract one or more data records from the file is received. The data records within the file are identified, without using prior knowledge of a structure of the file. The data records are then extracted.

Abstract: A computer-based system computes a probabilistic bound on the error probability of a nearest neighbor classifier as follows. A subset of the examples in the classifier is used to form a reduced classifier. The error frequency of the reduced classifier on the remaining examples is computed as a baseline estimate of the error probability for the original classifier. Additionally, subsets of the examples outside the reduced classifier are combined with the reduced classifier and applied to the remaining examples in order to estimate the difference in error probability for the reduced classifier and error probability for the original classifier.

Abstract: An improvement to an automatic data extractor has the capability of discovering new values that are not recognized by the vocabulary of the automatic data extractor and adding them to the record being formed and to the vocabulary, thus accumulating new vocabulary through use. The extractor gleans new values by deducing them from the structure of the text data and learns them by adding them to its vocabulary. The data extractor determines the structure of the data in much the same way as prior art data extractors but then a discovery process is used to identify a series of field lists using preferably at least one field parser and a field grader. The results of the grader are returned to an attribute mapper that identifies the position in the field list for each of the attributes.

Abstract: An event detection system allows data to be inserted while event conditions are being checked. Each record is assigned a time stamp as it is inserted into a database. Each event condition check is assigned a time stamp range. The event condition check then produces only those matches that have at least one record with a time stamp in the range and no record with a time stamp after the range. After each event condition check, the range is changed so that, in subsequent checks, no part of a previous range is duplicated and no time stamps are excluded from every checked range. As a result of this process, records may be inserted while event conditions are being checked.

Abstract: An improvement to an automatic data extractor has the capability of discovering new values that are not recognized by the vocabulary of the automatic data extractor and adding them to the record being formed and to the vocabulary, thus accumulating new vocabulary through use. The extractor glean s new values by deducing them from the structure of the text data and learns them by adding them to its vocabulary. The data extractor determines the structure of the data in much the same way as prior art data extractors but then a discovery process is used to identify a series of field lists using preferably at least one field parser and a field grader. The results of the grader are returned to an attribute mapper that identifies the position in the field list for each of the attributes.

Abstract: A set of in-sample examples and a method to measure distance between inputs are used by a computer-based system to compute a probabilistic bound on the error probability of the nearest neighbor classifier comprising the in-sample examples and the distance measure, as follows. The error frequency of a reduced classifier comprising a subset of the in-sample examples is computed. The in-sample examples not in the reduced classifier are partitioned into two holdout subsets. For each holdout subset, the difference in error frequency between the reduced classifier augmented with the holdout subset and the reduced classifier is computed. In the process, these difference frequencies are double-counted for some inputs that are closer to examples in each holdout subset than to any example in the reduced classifier.