Abstract: A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including a forecasting device configured to forecast hidden evidence found in relation to a user input in hidden cycle measurements into future forecasted cycle measurements, where the forecasted hidden cycles are transformed into an amplitude versus frequency histogram with each histogram being compared to each other histogram and determined if causation is a candidate, and if causation is a candidate, a probability density function is applied to produce a degree of causation of a causation link for the hidden evidence in relation to the user input.

Abstract: A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference.

Abstract: A method, system, and non-transitory computer readable medium for hidden evidence correlation and causation linking including computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements, forecasting the hidden evidence into future forecasted cycle measurements, and computing a causation link based on the future forecasted cycle measurements.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing a cognitive guide service to a group of participants for an excursion with an initial route planned by participants registration information and environment information along the initial route. During the excursion, real time sensory data on the participants and change in environment are collected by a cognitive agent accompanying the group to lead the excursion are relayed to a cognitive guide engine, and real time multi-objective optimization is modeled and performed. The participants are regrouped responsive to objectives of the excursion as represented by respective levels of interest in certain stage of the excursion as well as circumstances of the environment. Respective subgroups are formed from the participants per respective objectives, and respective new routes are selected from a set of optimal solutions for each subgroup.

Abstract: An edge device is dynamically reconfigured to block undesired traffic using control information that originates in a core network. The control information is delivered to the device indirectly and, in particular, by a core appliance (e.g., an intrusion prevention system) setting and returning an HTTP cookie to a requesting client. The edge device is pre-configured to respond to HTTP cookies that have (or that are) control information. When the receiving client later returns that cookie to the edge device to obtain subsequent service, the control information that originated at the core is used by the device to deny that service. This indirect method of communicating the control information (from the core to the requesting client and then back to the device) enables the device to be reconfigured dynamically as needed to address network exploits or other threats.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service by use of a lighthouse structure further including a private display for subscribed data for an accessibility-layered communication service as encoded with a private key to make inaccessible to the spectators but to be decoded on a user device with the private key.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service by use of a lighthouse structure further including a private display for subscribed data for an accessibility-layered communication service as encoded with a private key to make inaccessible to the spectators but to be decoded on a user device with the private key.

Abstract: A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing a cognitive tour guide service to a group of participants for a tour with an initial route planned by participants registration information and environment information along the initial route. During the tour, real time sensory data on the participants and environment are collected by a cognitive agent accompanying the group to lead the tour are relayed to a cognitive tour guide engine, and real time multi-objective optimization is modeled and performed. The participants are regrouped responsive to their respective objectives of the tour as represented by respective levels of interest in certain stage of the tour as well as circumstances of the environment. Respective subgroups are formed from the participants per respective objectives, and respective new routes are selected from a set of optimal solutions for each subgroup.

Abstract: A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service by use of a lighthouse structure further including a private display for subscribed data for an accessibility-layered communication service as encoded with a private key to make inaccessible to the spectators but to be decoded on a user device with the private key.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing a cognitive tour guide service to a group of participants for a tour with an initial route planned by participants registration information and environment information along the initial route. During the tour, real time sensory data on the participants health characteristics, change in environment are collected by a cognitive agent accompanying the group to lead the tour are relayed to a cognitive tour guide engine, and real time multi-objective optimization is modeled and performed. The participants are regrouped responsive to their respective enjoyment and circumstances of the environment and, among a set of optimal solutions, a new route for the tour is selected based on participant preference modeled and trained by participant feedback. During the tour, the cognitive tour guide engine continuously optimizes route responsive to incoming real time sensory data.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing social media feed cognizant lighthouse display service for communicating to spectators, information in regard to social response to an event, the lighthouse display service further including a private display for subscribed event details as encoded with a key to make inaccessible to the spectators but to be decoded on a user device with the key.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: providing a cognitive tour guide service to a group of participants for a tour with an initial route planned by participants registration information and environment information along the initial route. During the tour, real time sensory data on the participants health characteristics, change in environment are collected by a cognitive agent accompanying the group to lead the tour are relayed to a cognitive tour guide engine, and real time multi-objective optimization is modeled and performed. The participants are regrouped responsive to their respective enjoyment and circumstances of the environment and, among a set of optimal solutions, a new route for the tour is selected based on participant preference modeled and trained by participant feedback. During the tour, the cognitive tour guide engine continuously optimizes route responsive to incoming real time sensory data.

Abstract: A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements, forecasting the hidden evidence into future forecasted cycle measurements, and computing a causation link based on the future forecasted cycle measurements.

Abstract: An edge device is dynamically reconfigured to block undesired traffic using control information that originates in a core network. The control information is delivered to the device indirectly and, in particular, by a core appliance (e.g., an intrusion prevention system) setting and returning an HTTP cookie to a requesting client. The edge device is pre-configured to respond to HTTP cookies that have (or that are) control information. When the receiving client later returns that cookie to the edge device to obtain subsequent service, the control information that originated at the core is used by the device to deny that service. This indirect method of communicating the control information (from the core to the requesting client and then back to the device) enables the device to be reconfigured dynamically as needed to address network exploits or other threats.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: generating a drift annealed time series prediction model based on training data. In one embodiment the generating may include: recording an ensemble of candidate models for at least one predictor variable of the training data responsive to creating the ensemble based on the training data by machine learning. The ensemble includes three candidate models represented by respective prediction function to formulate a potentially predictive relationship between a target variable and predictor variables in the training data. Respective candidate models in the ensemble is manipulated to adjust degrees associated with predictor variables such that respective new models take relative importance of predictor variables into account and the drift annealed time series prediction model based on the new models is produced.

Abstract: Data pertaining to a subject matter domain, a set of text strings forming a set of seeds, a description of a linguistic structure present in a language of the domain-related data, and a statistical model applicable to the domain-related data are received. A set of portions of the domain-related data is extracted, a portion in the set of portions forming a nugget. A nugget matches the statistical model according to a criterion, and conforms to the linguistic structure within a threshold degree. The nugget is scored according to a subset of a set of features found in the nuggets. A subset of nuggets is selected. A score of each nugget included in the subset of nuggets exceeds a score threshold. The subset of nuggets is combined to form a pseudo-document. The pseudo-document is submitted to an application for answering a question related to the domain.

Abstract: A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference.

Abstract: Data pertaining to a subject matter domain, a set of text strings forming a set of seeds, a description of a linguistic structure present in a language of the domain-related data, and a statistical model applicable to the domain-related data are received. A set of portions of the domain-related data is extracted, a portion in the set of portions forming a nugget. A nugget matches the statistical model according to a criterion, and conforms to the linguistic structure within a threshold degree. The nugget is scored according to a subset of a set of features found in the nuggets. A subset of nuggets is selected. A score of each nugget included in the subset of nuggets exceeds a score threshold. The subset of nuggets is combined to form a pseudo-document. The pseudo-document is submitted to an application for answering a question related to the domain.