Abstract: Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Abstract: Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configur

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Abstract: A method for automated booking of an on-demand service in a multimodal journey includes receiving information related to a multimodal trip plan for a commuter trip. The multimodal trip plan includes a plurality of segments, and the plurality of segments include an on-demand service segment that requires an on-demand service vehicle as the mode of transport. The method also includes receiving real-time information relating to a location of the commuter from one or more location tracking sensors, determining an estimated time of arrival of the commuter at a transfer point for transfer to the on-demand service segment by analyzing the real-time information, identifying a time of availability of an on-demand service vehicle at the transfer point (as a function of a time of booking), and determining an optimal time of booking of the on-demand service vehicle based on the estimated time of arrival and the time of availability.

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Abstract: This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

Abstract: Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Abstract: The disclosed embodiments illustrate methods and systems for managing allocation of transportation services. The method includes receiving real-time vehicle data of vehicles of a plurality of service types from sensors associated with the vehicles. The method further includes detecting an availability status of each of the vehicles based on at least the received real-time vehicle data and a service request for a vehicle of a preferred service type for travel by a customer, to determine available service types. The method further includes rendering, in response to the service request, at least the available service types that comprises at least a flexible service type, at a user interface of a customer-computing device. The method further includes allocating the transportation services of the service provider to the customer based on a selection of a service type from the rendered plurality of available service types by the customer.

Abstract: A clockless delay adaptation loop configured to adapt to random data includes a first and a second delay line, an autocorrelator, and a controller. The autocorrelator receives an input signal for the delay adaptation loop and the output from the first delay line, and includes a first logic circuit configured to output a first autocorrelation and a second logic circuit configured to output a second autocorrelation. The controller is configured generate a control signal for one of the first and second delay lines based on the first and second autocorrelations. In some examples, the first logic circuit is an XNOR gate, and the second logic circuit is an OR gate. In some examples, the OR gate can have a gain that is two times a gain of the XNOR gate. In some examples, an amplifier having two times the gain of the XNOR gate is coupled to the OR gate.

Abstract: A method may include receiving, at a listener channel, an event described by a message, determining, using the message, a subscriber of the listener channel, determining, using a delivery preference of the subscriber, an adapter to deliver the message, and dispatching, via the adapter, the message to the subscriber.

Abstract: A set of resource requests that each includes authorization-supporting data for receiving a requested resource can be received. For each request, augmenting data associated with part of the data is retrieved, and it is determined whether access is authorized based on the augmenting data and the authorization-supporting data. A machine-learning model is trained using representations of the set of resource requests and the authorization determinations. Additional requests are processed by the trained model to generate corresponding authorization outputs. One or more identifiers to flag for inhibition of resource access are determined based on the authorization outputs. Upon detecting that a new resource request to access a particular resource includes an identifier of the one or more identifiers, a new authorization output is generated to inhibit access to the particular resource.

Abstract: A clockless delay adaptation loop configured to adapt to random data includes a first and a second delay line, an autocorrelator, and a controller. The autocorrelator receives an input signal for the delay adaptation loop and the output from the first delay line, and includes a first logic circuit configured to output a first autocorrelation and a second logic circuit configured to output a second autocorrelation. The controller is configured generate a control signal for one of the first and second delay lines based on the first and second autocorrelations. In some examples, the first logic circuit is an XNOR gate, and the second logic circuit is an OR gate. In some examples, the OR gate can have a gain that is two times a gain of the XNOR gate. In some examples, an amplifier having two times the gain of the XNOR gate is coupled to the OR gate.

Abstract: A memory module system for a high-dimensional weight space neural network configured to process machine learning data streams using Bayesian Inference and/or Classical Inference is set forth. The memory module can include embedded high speed random number generators (RNGs). The memory module is configured to compute, store and sample neural network weights by adapting operating precision to optimize the computing effort based on available weight space and application specifications.

Abstract: The disclosed embodiments illustrate a method and a system for controlling KPI parameters of a transportation system. The method includes extracting historical commuting characteristics of one or more commuters, from a database server over a communication network. The method further includes generating a predictive model based on the extracted historical commuting characteristics. The method further includes generating a service schedule of one or more transportation services of the transportation system. The service schedule of the one or more transportation services may be generated by use of the generated predictive model, based on defined criteria of the transportation system. The method further includes controlling a KPI parameter of the transportation system to attain a desired KPI parameter of the KPI parameter, based on the generated service schedule, when the one or more transportation services are deployed at one or more time stamps.

Abstract: Methods and systems for feature selection (FS) in machine learning (ML) are provided. The filter and wrapper methods can be combined to provide a hybrid FS method and system. The data can be clustered using mini-batch K-means clustering and ranked using normalized mutual information (NMI). The wrapper method can include using either a feature inclusion process or a least-ranked feature exclusion process that eliminates least-ranked features one by one from the ranking list.

Abstract: Provided is a process including: receiving a data token to be passed from a first node to a second node; retrieving machine learning model attributes from a collection of one or more of the sub-models of a federated machine-learning model; determining based on the machine learning model attributes, that the data token is learning relevant to members of the collection of one or more of the sub-models and, in response, adding the data toke to a training set to be used by at least some members of the collection of one or more of the sub-models; determining a collection of data tokens to transmit from the second node to a third node of the set of nodes participating in a federated machine-learning model; and transmitting the collection of data tokens.

Abstract: A computer-implemented technique is described herein for performing an image-based search that allows a user to create a custom query image that expresses the user's search intent. The technique generates the query image based on one or more input images and/or one or more information items that describe at least one desired characteristic of the query image. The technique then submits the query image to a search engine, and, in response, receives a set of candidate images that match the query image. In one implementation, the technique constructs the query image using a decoder neural network that operates on a mixed latent variable vector. In one approach, the technique uses a generative adversarial network (GAN) to produce the decoder neural network.

Abstract: The disclosed embodiments illustrate a method for real time management of transportation services. The method includes determining a discrete control process with respect to a plurality of states of a vehicle. Each state in the plurality of states corresponds to a station and a count of vacant seats in the vehicle at the station. A set of rules is generated to maximize a key performance parameter of the vehicle, based on at least the determined discrete control process. A valuation parameter is determined, in real time, for one or more vacant seats in the vehicle at each of one or more subsequent stations based on the generated set of rules. The determined valuation parameter for the one or more vacant seats corresponding to each of the one or more subsequent stations is transmitted to a plurality of mobile computing devices associated with a plurality of commuters.

Abstract: Provided is a process including: writing, with a computing system, a first plurality of classes using object-oriented modelling of modelling methods; writing, with the computing system, a second plurality of classes using object-oriented modelling of governance; scanning, with the computing system, a set of libraries collectively containing both modelling object classes among the first plurality of classes and governance classes among the second plurality of classes to determine class definition information; using, with the computing system, at least some of the class definition information to produce object manipulation functions, wherein the object manipulation functions allow a governance system to access methods and attributes of classes among first plurality of classes or the second plurality of classes to manipulate objects of at least some of the modelling object classes; and using at least some of the class definition information to effectuate access to the object manipulation functions.