Abstract: Systems, methods, and computer program products may store, in a distributed cache, a rule associated with a plurality of accounts in a Real-Time Payments (RTP) network, the rule being stored in association with account data associated with the plurality of accounts; receive an account level exclusion directive associated with the account; store, in the distributed cache, the account level exclusion directive in association with the account; receive transaction data associated with a transaction in the RTP network between the account and another account; retrieve, from the distributed cache, the rule, the account level exclusion directive, and the account data associated with the account; exclude, based on the account level exclusion directive, use of the rule for processing the transaction; and process, without applying the rule, the transaction in the RTP network.

Abstract: System and methods include obtaining Wi-Fi network data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers and obtaining customer data for each customer associated with the plurality of Wi-Fi networks, the customer data including call-ins made by customers; aggregating and filtering the data; analyzing the aggregated and filtered data including correlating the call-ins made by customers to the Wi-Fi network data; predicting customer call-ins based on correlations made between the call-ins made by customers and the Wi-Fi network data; and initiating a customer outreach workflow prior to a predicted customer call-in.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data based on one or more use cases; determining any of predictions and alerts for the one or more use cases based on the analyzing; and performing one or more actions based on the any of predictions and alerts.

Abstract: Techniques for computing global feature explanations using adaptive sampling are provided. In one technique, first and second samples from an dataset are identified. A first set of feature importance values (FIVs) is generated based on the first sample and a machine-learned model. A second set of FIVs is generated based on the second sample and the model. If a result of a comparison between the first and second FIV sets does not satisfy criteria, then: (i) an aggregated set is generated based on the last two FIV sets; (ii) a new sample that is double the size of a previous sample is identified from the dataset; (iii) a current FIV set is generated based on the new sample and the model; (iv) determine whether a result of a comparison between the current and aggregated FIV sets satisfies criteria; repeating (i)-(iv) until the result of the last comparison satisfies the criteria.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for the network condition of each of the plurality of customers of one or more service providers; determining an internet service provider (ISP) outage based on a plurality of factors; and performing one of a plurality of resolution or notification actions.

Abstract: Techniques are described for executing machine learning models trained for specific operators with feature values that are based on the actual execution of a workload set. The machine learning models generate an estimate of benefit gain/cost for executing operations on data portions in the alternative encoding format. Such data potions may be sorted based on the estimated benefit, in an embodiment. Using cost estimation machine learning models for memory space, the data portions with the most benefits that comply with the existing memory space constraints are recommended and/or are automatically encoded into the alternative encoding format.

Abstract: Systems and methods of detecting a parallel Wi-Fi network or a parallel Wi-Fi access point include operating a new Wi-Fi network at a location; analyzing Wi-Fi at the location; determining whether there is a parallel Wi-Fi network or a parallel Wi-Fi access point operating at the location with the new Wi-Fi network based on the analyzing; and, responsive to determining there is the parallel Wi-Fi network or the parallel Wi-Fi access point at the location, performing one or more of i) causing resolution of the parallel Wi-Fi network or the parallel Wi-Fi access point and ii) providing a notification to a user associated with the location.

Abstract: System and methods include obtaining Wi-Fi network data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers and obtaining customer data for each customer associated with the plurality of Wi-Fi networks, the customer data including call-ins made by customers; aggregating and filtering the data; analyzing the aggregated and filtered data including correlating the call-ins made by customers to the Wi-Fi network data; predicting customer call-ins based on correlations made between the call-ins made by customers and the Wi-Fi network data; and initiating a customer outreach workflow prior to a predicted customer call-in.

Abstract: Techniques are described for executing machine learning models trained for specific operators with feature values that are based on the actual execution of a workload set. The machine learning models generate an estimate of benefit gain/cost for executing operations on data portions in the alternative encoding format. Such data potions may be sorted based on the estimated benefit, in an embodiment. Using cost estimation machine learning models for memory space, the data portions with the most benefits that comply with the existing memory space constraints are recommended and/or are automatically encoded into the alternative encoding format.

Abstract: The present disclosure relates to methods, systems, and apparatuses for determining item recommendations and receiving dynamic modifications to an item recommendation algorithm. The method includes receiving electronic data indicating a set of items, generating, using a recommendation engine executing on a processor, a first set of one or more item recommendations from the set of items, causing a client interface to be generated, the client interface comprising the one or more item recommendations and a plurality of interface controls, wherein selection of one of the plurality of interface controls causes a weight adjustment indication to be transmitted for at least one of the plurality of weights, receiving the weight adjustment indicator, adjusting at least one weight of the plurality of weights associated with the weight adjustment indicator, and generating a second set of one or more item recommendations using the adjusted at least one weight.

Abstract: Systems, methods, and computer program products may store, in a distributed cache, a rule associated with a plurality of accounts in a Real-Time Payments (RTP) network, the rule being stored in association with account data associated with the plurality of accounts; receive an account level exclusion directive associated with the account; store, in the distributed cache, the account level exclusion directive in association with the account; receive transaction data associated with a transaction in the RTP network between the account and another account; retrieve, from the distributed cache, the rule, the account level exclusion directive, and the account data associated with the account; exclude, based on the account level exclusion directive, use of the rule for processing the transaction; and process, without applying the rule, the transaction in the RTP network.

Abstract: Herein are techniques that train regressor(s) to predict how effective would a machine learning model (MLM) be if trained with new hyperparameters and/or dataset. In an embodiment, for each training dataset, a computer derives, from the dataset, values for dataset metafeatures. The computer performs, for each hyperparameters configuration (HC) of a MLM, including landmark HCs: configuring the MLM based on the HC, training the MLM based on the dataset, and obtaining an empirical quality score that indicates how effective was said training the MLM when configured with the HC. A performance tuple is generated that contains: the HC, the values for the dataset metafeatures, the empirical quality score and, for each landmark configuration, the empirical quality score of the landmark configuration and/or the landmark configuration itself. Based on the performance tuples, a regressor is trained to predict an estimated quality score based on a given dataset and a given HC.

Abstract: Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data based on one or more use cases; determining any of predictions and alerts for the one or more use cases based on the analyzing; and performing one or more actions based on the any of predictions and alerts.

Abstract: Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.

Abstract: A method, apparatus, and computer program product are disclosed for self-service design, scheduling, and delivery of user-defined reports regarding promotions. The method includes receiving, from a user device, a report type and report delivery information. Based on the report type, relevant data regarding the one or more promotions is collected, using which a report is generated. The method then outputs the generated report based on the report delivery information. Optionally, analytical insights, such as trends within the data, sample size, suitability of control data, and indications of statistical significance, are generated and included in the report. A corresponding apparatus and computer program product are also provided.

Abstract: Systems and methods of detecting a parallel Wi-Fi network or a parallel Wi-Fi access point include operating a new Wi-Fi network at a location; analyzing Wi-Fi at the location; determining whether there is a parallel Wi-Fi network or a parallel Wi-Fi access point operating at the location with the new Wi-Fi network based on the analyzing; and, responsive to determining there is the parallel Wi-Fi network or the parallel Wi-Fi access point at the location, performing one or more of i) causing resolution of the parallel Wi-Fi network or the parallel Wi-Fi access point and ii) providing a notification to a user associated with the location.

Abstract: The present disclosure relates to methods, systems, and apparatuses for providing electronic communications to client devices based on clustering and filtering candidates for inclusion in the electronic communications based on programmatically generated correlation metrics and thresholds associated with the clustering.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for Wi-Fi metric based alarms, each Wi-Fi metric based alarm being associated with detection of one of an offline Wi-Fi network of the plurality of Wi-Fi networks, an offline node of the Wi-Fi network, instability of the Wi-Fi network, congestion in the Wi-Fi network, and interference in the Wi-Fi network; determining the Wi-Fi metric based alarms based on the analyzing; and performing one or more actions based on the determined Wi-Fi metric based alarms.

Abstract: Techniques described herein propose a ROWID Elimination Rewrite that uses functional dependencies to substitute and/or eliminate ROWID pseudo-columns referenced in a query in order to reduce memory pressure and speed up processing.