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 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: 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 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: 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: 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: 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 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.

Abstract: Herein are techniques to generate candidate rulesets for machine learning (ML) explainability (MLX) for black-box ML models. In an embodiment, an ML model generates classifications that each associates a distinct example with a label. A decision tree that, based on the classifications, contains tree nodes is received or generated. Each node contains label(s), a condition that identifies a feature of examples, and a split value for the feature. When a node has child nodes, the feature and the split value that are identified by the condition of the node are set to maximize information gain of the child nodes. Candidate rules are generated by traversing the tree. Each rule is built from a combination of nodes in a tree traversal path. Each rule contains a condition of at least one node and is assigned to a rule level. Candidate rules are subsequently optimized into an optimal ruleset for actual use.

Abstract: Methods and apparatus, including computer programs encoded on computer storage media, for monitoring resource transfer are provided.

Abstract: A method of operating a heating ventilation and air conditioning (HVAC) system of a structure, includes collecting first sensor data corresponding to a parameter of the HVAC system, collecting second sensor data that is different than the first sensor data, and generating clustered data by clustering the first sensor data and the second sensor data into a plurality of data clusters with a controller. The method also includes forming a transactional dataset based on at least the first sensor data, the second sensor data, and the clustered data with the controller, performing association rule mining (ARM) on the transactional dataset to generate a plurality of rules for each data cluster of the plurality of data clusters with the controller, and changing an operating characteristic of the HVAC system based on the plurality of rules with the controller to optimize the parameter.

Abstract: The present invention permits automated on-screen identification and obfuscation of sensitive data. The protective system receives a user's attempt to access sensitive data in a session, determines the user's level of access to sensitive data, and then obscures any sensitive data for which the user must supply credentials by placing at least one data field block over the sensitive data before the sensitive data is displayed on a desktop. As such the sensitive data cannot be seen by the user. The system can receive at least one credential from the user and compare it to at least one protective analytics rule in order to determine if it should remove the block and allow the user to view the sensitive data.

Abstract: Various methods, apparatuses/systems, and media for implementing a release automation dashboard module are disclosed. A database that stores a set of Sarbanes-Oxley (SOX) audit rules. A processor is coupled to the database via a communication network. The processor creates a release; checks the release for violations against the set of Sarbanes-Oxley (SOX) audit rules; validates that the release is scanned for cyber vulnerabilities in accordance with an organization's established practices; and authorizes deployment of the release based on a determination that the one or more features from the release does not violate the set of SOX audit rules and that the one or more features from the release meet a predetermined threshold for the cyber vulnerabilities.

Abstract: The present disclosure is directed to analyzing received sets of computer data. Methods and apparatus consistent with the present disclosure may forecast that a received set of computer data does not include malware after allowing instructions included in that set of computer data to execute for an amount of time that does not exceed an allocated amount of time. Methods consistent with the present disclosure may instrument a set of received program code and allow instructions in that received set of program code to execute as instrumentation code collects information about the set of program code. This collected information may be compared with sets of known good data when determining whether a received set of program code is likely not to include malware. This collected information may be associated with “behaviors” performed by the received set of program code that may be identified using sets of contextual data.

Abstract: A system may include: a server comprising a rule cache; a user device communicably coupled to the server; a computer-readable medium comprising instructions that cause the server to: monitor a plurality of third-party data sources; obtain, via a queueing service, a plurality of pieces of content from the plurality of third-party data sources; for each piece of content, fetch a rule ID from a list of rule IDs on the user device, wherein the rule ID is fetched based on the content and a pre-selected setting on the user device; use the rule ID to fetch a rule from the rule cache, the rule comprising a script, the script comprising executable code; execute the script on the piece of content to determine if the piece of content matches the rule; and in response to determining that the piece of content matches the rule, send an alert to the user device.

Abstract: Embodiments provide a service request processing method, a related device, and a communications system in an IMS network. In some embodiments, because 5GC network slices correspond to different services, P-CSCFs in the IMS also correspond to different service information. After receiving a terminated service request sent by an I-CSCF, a first S-CSCF can obtain an address of a corresponding P-CSCF based on terminated service information carried in the terminated service request. Then, the first S-CSCF sends the terminated service request to the P-CSCF corresponding to the address, and the P-CSCF processes the terminated service request.

Abstract: Machine logic for causing a computer system to perform the following operations (not necessarily in the following order): (i) receiving, by a historical decisions and opinions data store, decisions and opinions that have been communicated, propagated and/or otherwise espoused by a first entity; (ii) receiving, by an influencer data store, a plurality of influencer data sets, with each influencer data set including information indicative of opinions expressed by a respectively corresponding influencer; (iii) performing, by reverse chain engine, reverse chaining using the following inputs: (a) data of the historical decisions and opinions data store, and (b) the plurality of influencer data sets; and (iv) predicting, by a prediction engine, a likely future decision of the first entity using output of the reverse chaining operation.

Abstract: Servers of a clinical trial support network have digital data processors which execute an automated tracking and prediction model for patient engagement. The model is linked with an interfacing tool used by doctor managing a group of patients and an interfacing tool used by patients for real time capture of events. The system processes factor values to generate and track in real time a patient engagement index. The system uses the factor values to maintain a taxonomy of factor values. The system is also programmed to determine values of characteristics derived from the factors, such as a characteristic indicating time-based trend of a factor. Another advantageous characteristic is impact of multiple occurrences of a factor.

Abstract: The computerized method is shown and includes obtaining input at a local electronic device, generating a first result by processing the input according to a first rule set, wherein the first result provides a current status or a predicted status of the electronic device and transmitting the first result or indicia thereof to a remote server computer system. The computerized method additionally includes receiving a communication from the remote server computer system, wherein the communication includes an instruction to enable a second rule set, responsive to receiving the communication, enabling the second rule set, obtaining subsequent input at the local electronic device, and processing the subsequent input according to the second rule set. The computerized method may further include performing a statistical analysis on the input, and generating a predicated status of the electronic device based on an extrapolation process using a result of the statistical analysis.

Abstract: An anti-cracking method and system for a cloud host, as well as a terminal device are provided according to the disclosure. The method includes: obtaining system logs of the cloud host; determining an IP which fails to log in the cloud host according to the system logs as a suspicious IP; tracking and determining the suspicious IP to be an attacker IP according to the number of times of consecutive login failure of the suspicious IP; and adding a first blocking rule to firewall settings of the cloud host; wherein the first blocking rule instructs to block a login operation of the attacker IP during a first preset blocking time. With the anti-cracking method for a cloud host of the disclosure, the brute-force cracking can be prevented proactively in a timely manner with only very few system resources occupied.

Abstract: The disclosure provides an approach for remediating false positives for a network security monitoring component. Embodiments include receiving an alert related to network security for a virtual computing instance (VCI). Embodiments include collecting, in response to receiving the alert, context information from the VCI. Embodiments include providing a notification to a management plane based on the alert and the context information. Embodiments include receiving, from the management plane, in response to the notification, an indication of whether the alert is a false positive. Embodiments include training a model based on the alert, the context information, and the indication to determine whether a given alert is a false positive.

Abstract: Implementations include receiving, by an application programming interface (API) server of a plurality of API servers, a prediction request from a client system, each of the plurality of API servers including a stateless server, selecting, by the API server, a model server from a plurality of model servers based on the prediction request, each of the plurality of model servers including a stateful server, calling, by the API server, the model server to execute inference using a ML model loaded to memory of the model server, receiving, by the API server, an inference result from the ML model, and sending, by the API server, the inference result to the client system.

Abstract: A computer-implemented method of calculating tax liability using automatically generated estimates uses a computing device connecting to a shared data store configured to store user-specific tax data therein. The computing device executes an estimation module configured to estimate user-specific tax data and write the estimated user-specific tax data to the shared data store. A tax calculation engine configured to read user-specific tax data from the shared data store and write calculated tax data to the shared data store is executed, wherein at least some of the read data comprises estimated user-specific tax data.

Abstract: In response to detecting that a user of a service provider requires assistance, a computer system determines a predicted user intent associated with the user based on monitoring activity associated with the user, and further determines that a first communication channel corresponds to the determined predicted user intent and causes a communication to be initiated with the user utilizing the first communication channel. The computer system determines a user-initiated intent based on analyzing input received from the user, and based on the user-initiated intent, determines if the user-initiated intent corresponds to the predicted user intent. In response to determining that the user-initiated intent does not correspond to the predicted user intent, the computer system determines that a second communication channel corresponds to the user-initiated intent and causes the communication with the user to be switched to the second communication channel.

Abstract: In examples, a system adaptively orders a set of regular expressions based on frequencies that respective regular expressions of the set of regular expressions match a set of messages, the adaptive ordering to produce an adaptively ordered set of regular expressions. The system determines, for a first message of the set of messages, whether a plurality of regular expressions of the adaptively ordered set of regular expressions match the first message. The system constructs a bi-directional graph representing the plurality of regular expressions that match the first message, and classifies a second message based on the adaptively ordered set of regular expressions and the bi-directional graph.

Abstract: A method, apparatus, and system provide the ability to act on cyber risks and reduce cyber attacks. System characteristics and system information for a cyber system are gathered. The system characteristics and system information are pre-processed to identify vulnerabilities that are relevant to the cyber system. A system model of a cyber environment is generated for the cyber system. One or more features are converted from cyber threat reports to one or more semantically relevant queries over the system model. The system model is reasoned over to generate one or more answers relevant to the one or more semantically relevant queries. Attack models are executed over the system model to generate actionable intelligence and reduce cyber attacks.

Abstract: Systems and techniques that facilitate on-the-fly reorganization of directed acyclic graph nodes of a computing service for high integration flexibility are provided. In various embodiments, an input component can receive from a computing client a requested response time to be satisfied by a computing service. In various embodiments, a node property component can access an execution matrix. In various cases, the execution matrix can list execution properties of a plurality of nodes corresponding to the computing service. In various instances, the plurality of nodes can respectively perform a plurality of sub-operations associated with the computing service. In various aspects, the plurality of nodes can include one or more multi-level-of-execution subsets of nodes. In various cases, each multi-level-of-execution subset can contain at least two nodes that facilitate a same sub-operation at different levels of resource utilization.