Abstract: An explainable artificially intelligent (XAI) application contains an ordered sequence of artificially intelligent software modules. When an input dataset is submitted to the application, each module generates an output dataset and an explanation that represents, as a set of Boolean expressions, reasoning by which each output element was chosen. If any pair of explanations are determined to be semantically inconsistent, and if this determination is confirmed by further determining that an apparent inconsistency was not a correct response to an unexpected characteristic of the input dataset, nonzero inconsistency scores are assigned to inconsistent elements of the pair of explanations.

Abstract: A system for cognitive assessment of the competitiveness of deals may include a memory having stored thereon historical deal information for historical deals with each historical deal including a historical deal component. A historical deal component may include a historical work scope and associated historical work pricing. The system may also include a processor cooperating with the memory and configured to compare current deal information with the historical deal information. The current deal information may include a current deal component that may include a current work scope and associated current work pricing. The processor may use machine learning to determine whether the current deal component is non-competitive based upon the historical deal information, and for each non-competitive current deal component generate an alternative current deal component. The alternative current deal component may have at least one of a different current work scope and different associated current work pricing.

Abstract: Embodiments relate to systematic explanation of neural model behavior and effective deduction of its vulnerabilities. Input data is received for the neural model and applied to the model to generate output data. Accuracy of the output data is evaluated with respect to the neural model, and one or more neural model vulnerabilities are identified that correspond to the output data accuracy. An explanation of the output data and the identified one or more vulnerabilities is generated, wherein the explanation serves as an indicator of alignment of the input data with the output data.

Abstract: A method for selecting a set of information technology (IT) services peer deals to an in-flight deal for each first level service in the in-flight deal includes receiving a detailed cost structure for historical information, in-flight deals information, market deals information, services baselines and deals metadata, and multiple peer deals for selection. For historical information and market deals information, all missing baselines for all services in the in-flight deal and all missing unit cost for services are augmented at the first level service. The multiple peer deals are classified into different clusters at the first level service. A closest cluster to the in-flight deal at the first level service is selected. For each second level service of the in-flight deal the method: classifies the selected peer deals into different clusters. A predetermined number of peer deals that appear in a largest number of the selected clusters is selected.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an energy consumption profile which spans multiple intervals in a period of time, and predicting a net energy demand of a consumer system over the period of time. Moreover, a first multiple is determined which, when applied to the received energy consumption profile, produces an updated energy consumption profile which corresponds to an amount of energy that is capable of satisfying the predicted net energy demand of the consumer system. A greatest amount of underprediction is estimated. A greatest amount of overprediction is also estimated. Furthermore, an initial state of an energy storage device electrically coupled to the consumer system is computed according to the updated energy consumption profile. The initial state of the energy storage device is also based on a second multiple applied to each of the greatest amount of underprediction, and the greatest amount of overprediction.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving information which corresponds to an offering, and generating an offering definition by defining basic properties of the offering. Defining the basic properties of the offering includes: defining one or more cost drivers which correspond to the offering, and defining one or more cost components which correspond to the offering. The basic properties of the offering are further used to generate one or more cost model output structures. The one or more cost model output structures are also merged with a single cost model, and the single cost model is updated. Other systems, methods, and computer program products are described in additional embodiments.

Abstract: A system for cognitive assessment of the competitiveness of deals may include a memory having stored thereon historical deal information for historical deals with each historical deal including a historical deal component. A historical deal component may include a historical work scope and associated historical work pricing. The system may also include a processor cooperating with the memory and configured to compare current deal information with the historical deal information. The current deal information may include a current deal component that may include a current work scope and associated current work pricing. The processor may use machine learning to determine whether the current deal component is non-competitive based upon the historical deal information, and for each non-competitive current deal component generate an alternative current deal component. The alternative current deal component may have at least one of a different current work scope and different associated current work pricing.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an offer request including one or more desired services, and selecting available offerings, each of which include at least one of the desired services. A determination is made whether available benchmarks exist for each of the at least one desired service included in each of the selected available offerings. For each desired service determined as not having available benchmarks, a draft benchmark is computed for each of a plurality of criteria. A confidence weight is also computed for each of the draft benchmarks. The available benchmarks, the draft benchmarks, and the confidence weights are further used to construct an offer which is submitted in response to the received offer request. Moreover, the draft benchmarks and the corresponding confidence weights are re-computed for each of the respective desired services in response to determining that the submitted offer was not accepted.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an energy consumption profile which spans multiple intervals in a period of time, and predicting a net energy demand of a consumer system over the period of time. Moreover, a first multiple is determined which, when applied to the received energy consumption profile, produces an updated energy consumption profile which corresponds to an amount of energy that is capable of satisfying the predicted net energy demand of the consumer system. A greatest amount of underprediction is estimated. A greatest amount of overprediction is also estimated. Furthermore, an initial state of an energy storage device electrically coupled to the consumer system is computed according to the updated energy consumption profile. The initial state of the energy storage device is also based on a second multiple applied to each of the greatest amount of underprediction, and the greatest amount of overprediction.

Abstract: Systems, methods, and computer program products for providing personalized recommendations of devices for monitoring and/or managing a health condition are disclosed, and generally include receiving first structured information regarding a patient and a first set of one or more patient populations; receiving unstructured information regarding at least the patient and a second set of one or more patient populations; analyzing the unstructured information to derive second structured information; determining one or more health metrics to be monitored for the patient based on analyzing each of the first structured information and the second structured information, using a classification model; and determining an optimum set of devices to be used for monitoring the one or more health metrics. In some embodiments, metrics may be continuously monitored to detect a change exceeding an event trigger threshold, and a new set of recommended devices may be generated.

Abstract: A system for cognitive prioritization for report generation may include a processor and a memory cooperating therewith. The processor may be configured to accept a request for a new report from a user, the request having a user profile importance associated therewith and generate a predicted completion time for the new report based upon a historical completion time prediction model based upon historical data for prior reports. The processor may be configured to generate a predicted importance of the new report based upon a historical importance prediction model based upon the historical data for prior reports and determine a combined predicted importance based upon the user profile importance and the predicted importance. The processor may also be configured to generate a prioritization of the new report among other reports based upon the predicted completion time and the combined predicted importance and generate the new report based upon the prioritization.

Abstract: A method for selecting a set of information technology (IT) services peer deals to an in-flight deal for each first level service in the in-flight deal includes receiving a detailed cost structure for historical information, in-flight deals information, market deals information, services baselines and deals metadata, and multiple peer deals for selection. For historical information and market deals information, all missing baselines for all services in the in-flight deal and all missing unit cost for services are augmented at the first level service. The multiple peer deals are classified into different clusters at the first level service. A closest cluster to the in-flight deal at the first level service is selected. For each second level service of the in-flight deal the method: classifies the selected peer deals into different clusters. A predetermined number of peer deals that appear in a largest number of the selected clusters is selected.

Abstract: One embodiment provides a method for estimating unit price reduction of services in a new in-flight deal using data of historical deals and market reference deals cost structures. The method includes receiving a detailed cost structure for historical information, market deals information, services quantity information and deals metadata for a first year. For each service: peer deals to the in-flight deal are selected based on the detailed cost structure; missing cost data values in the peer deals are augmented; unit cost reduction values for the peer deals estimated; the unit cost reduction for the in-flight deal from each year in total contract years to a next year without a last contract year are estimated; and a total cost for the in-flight deal for all years in the total contract years beyond the first year are estimated.

Abstract: Computer program products are configured to perform methods for determining likely health conditions based on demographic information and/or determining appropriate wearable technology and services to monitor a patient's health. In one embodiment, a computer program product is configured to perform a method including receiving historical demographic data comprising a plurality of attributes; associating the historical demographic data with labels corresponding to known causes of particular health conditions; building a decision tree model using the historical demographic data and the associated label(s); generating a vector Yk using the model, Yk representing probable causes of a plurality of health conditions; and determining likely health conditions for a patient based on comparing the vector Yk to a second vector Zk, Zk representing probable causes of health conditions determined based on a health care record for the patient.