Computer-implemented methods and systems for quantifying appropriate machine learning model complexity corresponding to training dataset are provided. The method comprises monitoring, using one or more processors, N observed variables, v.sub.1 through v.sub.N, of a training dataset for a machine learning model; translating the N observed variables into m equisized bin indexes which generate m.sup.N possible equisized hypercells to estimate a fundamental dimensionality for the dataset; generating one or more samples by assigning a record in the dataset with numbers j through k as set id; generating a merged sample Si, for one or more values of the set id i, where i goes from j to k; and computing a fractal dimension of the equisized hypercube phase space based on count of cells with data coverage of at least one data point.

According to certain aspects of the disclosure, a computer-implemented method may be used for information discovery recommendation. The method may include receiving a query for a requested data card and determining information contained on a set of data cards other than the requested data card. Additionally, categorizing the information into a plurality of dimensions of data and matching the dimensions of data with information contained on the requested data card. Additionally, applying a weighting value to each of the matched plurality of dimensions of data and determining a combined weight total for each of the data cards. Additionally, determining at least one recommended data card with the highest combined weight total and displaying a user interface indicating at least one recommended data card is available. Additionally, presenting the at least one recommended data card based on a user interaction with the user interface.

Synthesizing a control object for a computing event, the control object for securing a computing resource based on a set of access and privilege information provided through a set of mediated associations that are represented by an enchained set of certificates, portions of which are encrypted including entity-specific paths to entity-specific predecessor certificates and partial decryption keys therefor, wherein the control object is applied to secure the computing resource for performing a computing action indicated by a process-type entity identified in the certificate for the control object.

A system may provide virtual energy audits of one or more target buildings. The system may retrieve weather data and energy usage data specific to a given target building from a weather server and a utility server, respectively. The system may store predefined building characteristics corresponding to the given target building in local memory. Based on the weather data, energy usage data, and/or predefined building characteristics, the system may generate one or more building markers that characterize the energy usage and efficiency of the given target building. Building efficiency diagnostics and energy conservation prognostics may be generated based on the building markers and may be sent by the system to be displayed via a user interface of a client device. The energy conservation prognostics may include one or more energy conservation measure recommendations and corresponding predicted cost/energy savings.

Systems and methods for generating training data for a supervised topic modeling system from outputs of a topic discovery model are described herein. In an embodiment, a system receives a plurality of digitally stored call transcripts and, using a topic model, generates an output which identifies a plurality of topics represented in the plurality of digitally stored call transcripts. Using the output of the topic model, the system generates an input dataset for a supervised learning model by identify a first subset of the plurality of digitally stored call transcripts that include the particular topic, storing a positive value for the first subset, identifying a second subset that do not include the particular topic, and storing a negative value for the second subset. The input training dataset is then used to train a supervised learning model.

In one embodiment, a method includes receiving a user query inputted on a head-mounted device from the head-mounted device, wherein the user query corresponds to multiple dialog-intents, executing multiple tasks corresponding to the multiple dialog-intents, generating a multi-perspective response by a stitching model based on two or more of execution results of the multiple tasks, wherein the stitching model combines the two or more of the execution results based on natural language processing, and wherein the multi-perspective response comprises a natural-language response combining the two or more execution results, and sending instructions to the head-mounted device for presenting the multi-perspective response on the head-mounted device.

Apparatuses, methods, and systems are disclosed for configuring an AI based framework. One method includes receiving a first indication indicating an AI based framework. The method includes receiving configuration information corresponding to CSI report settings. The configuration information includes CSI RSs to be received at a UE; and the CSI RSs are transmitted over a frequency index, a spatial index, and/or a temporal index, and decomposed into two groups based on the frequency index, the spatial index, and/or the temporal index. The method includes generating first CSI based on a first group of the two groups of CSI RSs. The method includes transmitting a CSI report including the first CSI. The method includes communicating an AI based report corresponding to second CSI corresponding to a second group of the two groups of CSI RSs. The first AI based report for CSI includes a correlation between the first and second CSI.

An apparatus comprises a processing device configured to detect a request for parameter values to be utilized in a given iteration of performance testing of an information technology (IT) asset in an IT infrastructure, to determine a current state of the IT asset, the current state comprising two or more performance metric values, and to generate, utilizing a reinforcement learning framework, the parameter values to be utilized in the given iteration of the performance testing of the IT asset based at least in part on the current state. The processing device is also configured to perform the given iteration of performance testing of the IT asset utilizing the generated parameter values, and to update the reinforcement learning framework based at least in part on a subsequent state of the IT asset following the given iteration of performance testing of the IT asset.

A content management system may support a card engine to dynamically perform operations such as configuring content for display via a user interface and generating reports based on user behavior, account status, and business logic. In cooperation with a facts controller to provide facts that the card engine may access substantively in real time, a rules engine to provide constructs in the form of card definitions, and a development engine, the content management system may enable a content manager to effect changes to card and container definitions by providing or modifying rules and rulesets in the rules engine dynamically. Cards evaluated dynamically by the card engine may be transmitted to user equipment. In this way, the content manager may make content decisions in accordance with business logic and events occurring proximate to the user, thereby impacting the user experience and generating reports in a substantive and real-time fashion.

There is provided a risk assessment apparatus having a model acquisition part that acquires at least one explainable predictive model; a risk determination part that determines risk in the at least one model on the basis of the at least one model and ethical risk factor information, which is information that is an ethical risk factor; a model selection part that selects a model on the basis of the result of risk determination; and a model output part that outputs the selected model.