Abstract: An intention detection mechanism detects a target user accessing an operational context and accesses sensor data collected by a plurality of sensors encoding signals describing one or more characteristics of the target user and surroundings of the target user. The intention detection mechanism converts the accessed sensor data into a multi-channel sensor data time series, where each channel represents a characteristic of the target user. The intention detection mechanism compares the sensor data time series to a multi-channel intentionality template to determine whether the sensor data time series contains the multi-channel intentionality template. The multi-channel intentionality template describes characteristics indicative of an intent of a target user accessing the operational context. If the sensor data time series contains the multi-channel intentionality template, the intention detection mechanism communicates a signal confirming that the target user intended to access to the operational context.

Abstract: Certain aspects of the present disclose provide techniques for generating a knowledge engine module collection. Techniques for generating the module collection include receiving input data comprising a first identifier, a second identifier, and a third set of fields. Based on the input data, a UI builder tool can retrieve a first set of artifact files and a second set of artifact files corresponding to a first module and a second module. The UI builder tool can generate a third set of artifact files based on the first set of artifact files, the second set of artifact files, and the input data.

Abstract: A vehicle and a system and a method of operating the vehicle. The system includes a reasoning engine, an episodic memory, a resolver and a controller. The reasoning engine infers a plurality of possible scenarios based on a current state of an environment of the vehicle. The episodic memory determines a historical likelihood for each of the plurality of possible scenarios. The resolver selects a scenario from the plurality of possible scenarios using the historical likelihoods. The controller operates the vehicle based on the selected scenario.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data transformations, according to embodiments of the present invention. In one embodiment, a set of example values including example input values that indicate data values to be transformed and example output values that indicate a desired form in which to transform data. Based on the set of example values, a data transformation function that is relevant to the set of example values is identified. The data transformation function is used to generate a transformation program to transform the example input values to the desired form in which to transform data. A suggestion of the transformation program can be provided to a user device, wherein selection of the transformation program suggestion results in a data transformation.

Abstract: A security threat detection system is used to monitor the physical resource usage of a hosted application in a PaaS service in order to detect anomalous behavior indicative of a security threat. The system analyzes the historical usage of the application's physical resources in order to determine the normal range of consumption of a resource by the application. A security threat alert is then provided when the application's resource consumption exceeds the normal range of consumption.

Abstract: Example aspects of the present disclosure relate to an example computer-implemented method for predicting the intent of actors within an environment. The example method includes obtaining state data associated with a plurality of actors within the environment and map data indicating a plurality of lanes of the environment. The method include determining a plurality of potential goals each actor based on the state data and the map data. The method includes processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to determine (i) a forecasted goal for a respective actor of the plurality of actors, (ii) a forecasted interaction between the respective actor and a different actor of the plurality of actors based on the forecasted goal, and (iii) a continuous trajectory for the respective actor based on the forecasted goal.

Abstract: An orchestration system may receive, from a subscriber management system, a request indicating a subscriber intent for a connectivity service or a cloud service. The orchestration system may identify, based on subscriber information associated with the request, a managed service. The orchestration system may automatically discover subscriber network devices configured to access the managed service and configure network connectivity of the subscriber network devices. The orchestration system may allocate infrastructure and network resources for the managed service. The orchestration system may configure, based on the resources, infrastructure hardware components, software components, and applications of the managed service. The orchestration system may configure managed service components associated with the managed service. The orchestration system may connect the infrastructure hardware components, the software components, the applications, and the managed service components to service assurance systems.

Abstract: An automatic coordination apparatus 30 includes: a scenario creation unit 100 that creates a scenario; a scenario coordination unit 200 that coordinates a plurality of the scenarios; and a scenario management and execution unit 32 that manages and executes the scenario, wherein a failure handling business operation is broken down into common and individual tasks, a parent scenario or a child scenario is defined for each of the separated tasks or each task group made by combining a plurality of tasks, the scenario is divided, management across a plurality of the scenarios is performed based on a relationship between each scenario and a status of execution of each scenario, and the scenario is discontinued or changed depending on a process and a content of failure handling.

Abstract: A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to store a data structure of a digital twin of an entity of a building, the data structure storing information associated with at least one of the entity or entities of the building and determine that a trigger rule of the digital twin is triggered by comparing at least some of the information of the data structure against one or more conditions of the trigger rule. The instructions cause the one or more processors to determine an action of an action rule of the digital twin responsive to determining that the trigger rule is triggered by executing the action rule and cause one or more devices to operate based on the action determined by the action rule.

Abstract: Method and server apparatus for providing and using a centralized Link Metadata System to identify if navigational links are pop-out links, is provided. The present invention utilizes a Link Metadata System, available on the Internet, of metadata associated with any given, specific Internet domain and URI path combination. Software browsing user agents on remote window servers are then configured with the ability to request metadata from the Link Metadata System indicating that a link is a pop-out link. Upon receiving metadata, the user agent determines whether to perform navigation as normal or perform the pop-out link functionality of reloading the remote client user agent's top level container with the content provided at the link's destination URI.

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

Abstract: A knowledge-based system under uncertainties and/or incompleteness, referred to as augmented knowledge base (AKB) is provided, including constructing, reasoning, analyzing and applying AKBs by creating objects in the form E?A, where A is a rule in a knowledgebase and E is a set of evidences that supports the rule A. A reasoning scheme under uncertainties and/or incompleteness is provided as augmented reasoning (AR).

Abstract: An artificial intelligence (“AI”) engine is disclosed with AI-engine modules and a plurality of learning agents. The AI-engine modules include instructor, learner, and predictor modules. The learner module is configured to train a plurality of AI models in parallel, and the instructor module is configured to coordinate with a plurality of simulators for respectively training the AI models. The learning agents are configured to process training requests from the instructor on data from the simulators for training the AI models. The learner module is further configured to first train the AI models on a first batch of similar data synchronously pooled in a memory of the learner module with a first processor. The learner module is further configured to subsequently train the AI models on a second, different batch of similar data synchronously pooled in the memory of the learner module with the first processor.

Abstract: A support service application may process a request that contains a hyperlink. The hyperlink may be examined to determine a level of risk associated with accessing a resource referred to by the hyperlink. An execution environment for processing the hyperlink may be identified. The execution environment may comprise a virtual machine drawn from a pool of virtual machines. The resource may be retrieved by a browser process operating within the context of the virtual machine, and a safe version of the resource may be rendered and made available to customer support personnel using the support service application.

Abstract: This disclosure describes methods and systems for protecting machine learning models against privacy attacks. A machine learning model may be trained using a set of training data and causal relationship data. The causal relationship data may describe a subset of features in the training data that have a causal relationship with the outcome. The machine learning model may learn a function that predicts an outcome based on the training data and the causal relationship data. A predefined privacy guarantee value may be received. An amount of noise may be added to the machine learning model to make a privacy guarantee value of the machine learning model equivalent to or stronger than the predefined privacy guarantee value. The amount of noise may be added at a parameter level of the machine learning model.

Abstract: Computerized systems and methods for managing API information. An exemplary method includes receiving an input from a user device associated with a first computer system, the input not including identity of a second computer system. The method includes determining a target API based on the input, the target API being the second computer system's API. The method also includes determining whether a user of the user device has access to the target API. The method includes retrieving documentation of the target API from an API database if it is determined that the user has access to the target API. The method includes providing the user device with the retrieved documentation.

Abstract: A method and associated systems for a database-management system with artificially intelligent database administration. The DBMS manages one or more databases, each of which is monitored by sensors that detect conditions indicative of database performance. An operational engine receives input from the sensors, translates it into a form understandable by an artificially intelligent decision engine, and forwards the translated input to the decision engine. The decision engine uses preloaded knowledge elements stored in a knowledgebase to infer whether the sensor input identifies an issue that can only be resolved by a database-administration activity. If so, the decision engine attempts to select a best solution, optionally seeks confirmation of its selection from an outside source, and directs the operational engine to implement the selected solution.

Abstract: Some embodiments provide a program that receives a request to sectionize a document, uses a visual model to identify a set of candidate section headers in the document, and uses a language model to determine a type of section header for at least one candidate section header in the set of candidate section headers in the document. Some embodiments provide a program that receives a request to anonymize data in a document, uses a visual model to identify a set of candidate confidential sections in the document that are each predicted to include a collection of confidential data, uses a language model to identify terms in each candidate confidential section that are determined to be confidential data, analyzes the document to identify a set of terms in the document based on the identified terms in the set of candidate confidential sections, and redacts the set of terms in the document.

Abstract: A model checking system detects violations and conflicts in security and verification policies by running model checking processes. The system detects privilege escalation attacks in misconfigured identification and access management (“IAM”) policies by modeling security policy documents and IAM actions as logical formulas and then running model checking on the model. The system translates non-Boolean variables, such as string variables, into Boolean variables in order to apply an SAT model checker. The model checker also determines whether a policy violation can be achieved in a finite number of steps by elevating privileges of some compromised principal over multiple iterations of the model checking process, or proves absence thereof.

Abstract: Described is a system for detecting and correcting perception errors in a perception system. In operation, the system generates a list of detected objects from perception data of a scene, which allows for the generation of a list of background classes from backgrounds in the perception data associated with the list of detected objects. For each detected object in the list of detected objects, a closest background class is identified from the list of background classes. Vectors can then be used to determine a semantic feature, which is used to identify axioms. An optimal perception parameter is then generated, which is used to adjust perception parameters in the perception system to minimize perception errors.

Abstract: A graph data structure for an artificial intelligence knowledge base includes a plurality of user-centric facts associated with a user. Each user-centric fact has an application-agnostic data format and includes a subject graph node, an object graph node, and an edge connecting the subject graph node to the object graph node. The graph data structure is designed to accommodate facts from different application-specific data providers using the same application-agnostic data format even when the different application-specific data providers use different native data formats.

Abstract: The disclosed systems and methods track and continuously monitor data about a player or a multiple players and create a non-playing character (NPC) and/or modify an existing NPC that replicates the player(s) play style. The disclosed systems implement an artificial intelligence engine that monitors how a real player responds to one or more events in a game and correlates game outcomes with real player actions, with the actions or reactions of third players, and/or with an amount or extent of engagement. The engine may be used to generate, host, or otherwise provide data representative of one or more NPCs to multiple different games, being hosted by one or more servers, concurrently.

Abstract: A system and method allowing machine analytics and multiple human analysts to work together in a collaborative fashion in the analysis of large amounts of data. The collaborative analysis promotes “sensemaking.” Sensemaking is the continued revising and expansion of a developing “story” so that it becomes more comprehensible. As the emerging story is refined, it should successfully incorporate more and more of the observed data and be more defensible with respect to contrary arguments.

Abstract: Examples of the present disclosure describe systems and methods for behavioral threat detection definition. In an example, a behavior rule comprising a set of rule instructions is used to define one or more events indicative of a behavior. For example, a set of events from which one event must be matched may be defined or a set of events from which all events must be matched may be defined. In some examples, events are matched based on an event name or type, or may be matched based on one or more parameters. Exact and/or inexact matching may be used. The set of rule instructions ultimately specifies one or more halt instructions, thereby indicating that a determination as to the presence of the behavior has been made. Example determinations include, but are not limited to, a match determination, a non-match determination, or an indication that additional monitoring should be performed.

Abstract: A task management platform generates an interactive display tasks based on multi-team activity data of different geographic locations across a plurality of distributed guided user interfaces (GUIs). Additionally the task management platform uses a distributed machine-learning based system to determine a suggested task item for a remote team based on multi-team activity data of different geographic locations.

Abstract: In one embodiment, a method includes receiving a user request from a client device associated with a user, accessing an instructional file comprising one or more binary inference engines and one or more encrypted model data corresponding to the one or more binary inference engines, respectively, selecting a binary inference engine from the one or more binary inference engines in the accessed instructional file based on the user request, sending a validation request for a permission to execute the binary inference engine to a licensing server, receiving the permission from the licensing server, decrypting the encrypted model data corresponding to the binary inference engine by a decryption key, executing the binary inference engine based on the user request and the decrypted model data, and sending one or more execution results responsive to the execution of the binary inference engine to the client device.

Abstract: A question inference device comprises: an input unit inputting an inquiry from a user; a storage unit storing a plurality of questions prepared in advance and one or more keywords for identifying each question in association with each other; a choice unit referring to the storage unit and choosing a question associated with a keyword contained in the inquiry inputted by the input unit from among the plurality of questions; a computation unit computing the likelihood of each of the plurality of questions for the inquiry inputted by the input unit; an inference unit inferring, based on a choice result of the choice unit and a computation result of the computation unit, such a question that the user intends the substance of the question from among the plurality of questions; and an output unit outputting information based on an inference result of the inference unit.

Abstract: The present invention is a method and system for automatically producing a learning plan. Upon receiving at least one type of data input, the system analyzes the data input and produces a learning plan based on the results of the analysis of the data input. This process may be used to either generate or update a learning plan, and may be repeated to update an existing learning plan.

Abstract: A testing platform for an auto-decision engine is disclosed. An auto-test database for a decision engine may be accessed and modified by, for example, executing an application programming interface protocol of a third-party computing system. The decision engine may be bypassed, and a simulated auto-decision may be requested based on a unique key corresponding to a scenario in the auto-test database. A graphical user interface displays information regarding the auto-test database on a user computing device, and one or more output data fields in the auto-test database may be modified.

Abstract: A method is disclosed. A server computer may receive, from a client computer, a first rule comprising one or more variables, the first rule configured to predict that an event is either a first classification or a second classification. The server computer applies the first rule to an initial data set of events to identify events that are associated with the first classification. The server computer evaluates performance of the first rule. The server computer provides data relating to the performance of the first rule to the client computer. The client computer automatically evaluates the performance of the first rule and generates a second rule in response to the data relating to the performance of the first rule. The server computer may then evaluate the second rule and provide performance data to the client computer. The server computer may use equi-height histograms to evaluate the performance of the rules.

Abstract: A position debiased search system can avoid bias towards top-ranked search results using a position-trained machine-trained model. Past positions for listings can be input into the model with added noise and low-ranked results to train the model to generate rankings that do not exhibit position bias. A network site can implement the position debiased search system to generate network site results that can generate accurate user results in real time as users browse the network site.

Abstract: The present disclosure provides technical solutions related to intelligent cloud management based on profile. Artificial intelligent is applied to cloud management and cloud management suggestion may be proposed intelligently. In daily work, behaviors in using cloud resources may show characteristics of cloud users or cloud tenants themselves. The technical solution of intelligent cloud management of the present disclosure generates profile identifying cloud using characteristics by extracting behavior data in using cloud and intelligently proposes cloud management suggestions based on the profile.

Abstract: Systems and methods are described for training a machine learning model to make a series of sequential decisions, in which the results of previous decisions are known prior to the next decision in the sequence being made. A safe reinforcement learning model estimates the results of choosing various options for a first decision in the sequence, and further estimates the amount of information that will be gained by choosing each of the options. The estimated information gain associated with each option is then used to forecast how the remaining decisions in the sequence would be improved by using the gained information to improve the prediction model and make better decisions. The safe reinforcement learning model further incorporates decision constraints provided by subject matter experts, which may set requirements for the selection such as a minimum required result and allow the safe reinforcement learning model to explore options within those constraints.

Abstract: Computer systems and associated methods are disclosed to detect a future change point in time series data used as input to a machine learning model. A forecast for the time series data is generated. In some embodiments, a fitting model is generated from the time series data, and residuals of the fitting model are obtained for respective portions of the data both before and after a potential change point in the future. The change point is determined based on a ratio of residual metrics for the two portions. In some embodiments, data features are extracted from individual segments in the time series data, and the segments are clustered based on their data features. A change point is determined based on a dissimilarity in cluster assignments for segments before and after the point. In some embodiments, when a change point is predicted, an update of the machine learning model is triggered.

Abstract: A management system for managing a data transmission destination comprises an accumulation unit configured to accumulate, when data transmission is performed, transmission information of the data transmission as collected data; a generation unit configured to generate training data including a pair of ground truth data containing transmission destination information, and input data containing an item other than the transmission destination information, from the collected data accumulated in the accumulation unit; a processing unit configured to generate, by machine learning, a learning model to be used to predict a transmission destination, by using the training data generated by the generation unit; and a providing unit configured to provide a prediction process using the learning model in response to a request.

Abstract: Systems and methods are described for a decision-making process including actions characterized by stochastic availability, provide an Markov decision process (MDP) model that includes a stochastic action set based on the decision-making process, compute a policy function for the MDP model using a policy gradient based at least in part on a function representing the stochasticity of the stochastic action set, identify a probability distribution for one or more actions available at a time period using the policy function, and select an action for the time period based on the probability distribution.

Abstract: In some implementations, a method is provided. The method includes receiving captured packet traffic, the captured packet traffic including a plurality of packets transmitted over a network. One or more communication patterns for each of one or more levels in a network stack are detected based on metadata of the captured packet traffic, each communication pattern indicating communication between two components in the network. The method further includes generating a topology of the network in view of the one or more communication patterns detected for each of the one or more levels in the network stack.

Abstract: A computer-implemented method for unambiguously identifying entities in a database system may be provided. The method comprises storing data items as records with different attributes in a table of a database, storing naming rules for selected combinations of the attributes of the data items, and prioritizing the naming rules. The method also comprises determining a hash value for each of the selected combinations of the attributes of the data items, and identifying duplicate data items using the determined hash values and the prioritized naming rules.

Abstract: An analyzing apparatus includes a memory unit that stores analysis conditions of each standards specifying analysis conditions for each analytical technique, a control unit, and an analysis unit that performs an analysis in accordance with a predetermined analytical technique. The control unit reads out the analysis conditions of the standard, and the control unit displays analysis conditions in time sequence, the displayed analysis conditions being necessary for the analysis and selected from out of the read-out analysis conditions, or the control unit prompts to input the analysis conditions in time sequence. When the analysis conditions are input, the control unit determines whether or not the input analysis conditions comply with the read-out analysis conditions, and in a case where a result of the determination is positive, the control unit displays a next analysis condition being necessary for the analysis, or the control unit prompts to input the next analysis condition.

Abstract: Methods, systems and computer program products are provided for automated runtime configuration for dataflows to automatically select or adapt a runtime environment or resources to a dataflow plan prior to execution. Metadata generated for dataflows indicates dataflow information, such as numbers and types of sources, sinks and operations, and the amount of data being consumed, processed and written. Weighted dataflow plans are created from unweighted dataflow plans based on metadata. Weights that indicate operation complexity or resource consumption are generated for data operations. A runtime environment or resources to execute a dataflow plan is/are selected based on the weighted dataflow and/or a maximum flow. Preferences may be provided to influence weighting and runtime selections.

Abstract: A computer specific system for receiving a plurality of medical imaging specific 3D patient specific data sets from different 3D data sources, for a patient receiving reconstructive surgery, locating and applying a plurality of landmarks to each data set, and performing an overlay analysis procedure that aligns the 3D data sets from the different 3D data sources to create a 3D medical image representation of the patient's tissue. The 3D representation can be used to create 3D models for use by surgeons to perform reconstructive surgical procedures.

Abstract: Technologies for user-assisted machine learning includes a compute device configured to request user assistance to classify sensor data in response to a determination that a confidence score associated with the classification of the sensor data is below a threshold value and/or if the classification of the sensor data is unknown. In an illustrative embodiment, the compute device is configured to communicate with an activity monitor device, such as a smart pet collar, to determine activities of the subject (e.g., a pet) based on classification data received from the smart pet collar.

Abstract: A dual memory Secure Digital (SD) card is provided which allows for remote data updates without disruption to a currently executing program, as well as a system and method that utilize the dual memory SD card. The dual memory SD card may include a primary memory, an independent secondary memory, and a microcontroller or Application Specific Integrated Circuit (ASIC) that can load either memory upon boot up of a host computer. The dual memory SD card may also include a wireless interface, such as Wi-Fi or Bluetooth, in addition to a standard SD pin interface. An automated data synchronization system is provided which allows a new version of data to be uploaded onto the secondary memory of the dual memory SD card while an existing data version is running on that same dual memory SD card and swapped into operation upon the next reboot of a host device.

Abstract: A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.

Abstract: A knowledge-based system under uncertainties and/or incompleteness, referred to as augmented knowledge base (AKB) is provided, including constructing, reasoning, analyzing and applying AKBs by creating objects in the form E?A, where A is a rule in a knowledgebase and E is a set of evidences that supports the rule A. A reasoning scheme under uncertainties and/or incompleteness is provided as augmented reasoning (AR).

Abstract: A method is disclosed for enabling a network location to provide an ordering process for data relevant to connected network devices' activities. The method includes assembling the data, utilizing the activity data, and associating the data, such that information is derived to enable a desired expansion of at least one designated activity. Another method is disclosed for managing an object assignment broadcast operations for a network location based on a network device's previous activities. This second method includes tracing a network device's conduct to determine that a network device prefers a particular class of content. The method also includes tagging a network device's profile with the respective observation and deciding by a network location as to the classification factor for a network device to be targeted for an object assignment broadcast.

Abstract: A system and method for managing implementation of policies in an information technologies system receives at least one policy function, at least one refinement template and at least one available policy function from the at least one memory, receives a policy input indicating a high-level policy for the IT system where the policy input is compliant with the at least one policy function and is received in a format that is not machine-enforceable at an enforcement entity of the IT system, based on the received policy input, automatically or semi-automatically generates a machine-enforceable rule and/or configuration by filling the at least one refinement template, where the machine-enforceable rule and/or configuration includes the at least one available policy function and being compliant with the received policy input, and distributes the machine-enforceable rule and/or configuration to the at least one memory of the IT system or another at least one memory to thereby enable implementation of the policies.

Abstract: Provided are techniques for data analysis and rule generation for providing a recommendation. Current features are identified from data in a corpus. In response to receiving an indication that the data has changed, a new feature is identified. A feature set is created by identifying one or more related features of the current features. A feature worthiness score for the feature set is generated. In response to the feature worthiness score exceeding a threshold, the feature set is input to a model. One or more rules from the model are received, where each of the one or more rules includes the one or more related features, the new feature, and a recommendation. In response to receiving a set of values for the one or more related features and the new feature, a rule of the one or more rules is applied to provide the recommendation for that set of values.

Abstract: Systems and methods for generating concept-based search summaries from a plurality of documents are provided. In one embodiment, a system may include interfaces to receive information identifying a meaning taxonomy including a normalized term and a search query including search terms. The system may be configured to identify documents relating to the search terms and normalized terms and display a concept-based summary of the documents, the summary including a syntactic structure associated with the normalized terms and search terms. In another embodiment, a method includes receiving a meaning taxonomy including normalized terms and search terms, identifying at least one document including the search terms and syntactic structures associated with the normalized terms, and display a search summary including the search terms and syntactic structures.

Abstract: Systems and methods for content management that allow external events or data to drive the generation of content are disclosed. Specifically, disclosed embodiments allow events generated from external sources (or data determined from those events, or otherwise determined) to drive the generation of content (or metadata associated with that content) in a content management system.