Abstract: Embodiments of the present invention provide a computer system a computer program product, and a method that comprises converting received data from a time-based domain to a frequency-based domain using a signal processing algorithm; identifying transactional noise within the converted data by identifying contextual factors based on a determined pattern, wherein the transactional noise is data associated with an identified fraudulent transaction; filtering the identified transactional noise by removing datapoints within the converted data that reaches a predetermined threshold of signal strength using the signal processing algorithm; and generating a line graph depicting removal of the data that is indicative of the identified transactional noise from the converted data.

Abstract: A system, computer program product, and method are presented for facilitating determinations of risk including behavior classifications and predictions through timeline reshaping and rescoring of structured data. One embodiment of the method includes receiving, for one or more target focal objects, at least a portion of a transaction history including a plurality of sequential transactions, where the portion of the transaction history is associated with a first temporal range. The method also includes generating a first transaction timeline image representative of the portion of the transaction history, where the first temporal range includes a first temporal scaling. The method further includes labeling, through a machine learning (ML) model, the first transaction timeline image. The method also includes reshaping the first transaction timeline image, including rescaling the first temporal range, thereby generating a rescaled transaction timeline image, and labeling the rescaled transaction timeline image.

Abstract: One or more computer processors determine a plurality of models to incorporate a plurality of determined features from a received dataset. The one or more computer processors generate an aggregated prediction utilizing each model, in parallel, in the determined plurality of models subject to stop criteria, wherein stop criteria includes a prediction duration threshold. The one or more computer processors calculate a confidence value for the aggregated prediction.

Abstract: A system receives transaction parameters which indicate a type of fraud. The system generates a set of sample transactions based on the parameters. The set of sample transactions generated by the system include at least one fraudulent transaction consistent with the type of fraud indicated by the parameters. The system can then send the transaction to an analyzer. Upon receiving results from the analyzer, the system evaluates performance of the analyzer.

Abstract: One or more computer processors select a plurality of key-events contained in a dataset. The one or more computer processors determine a plurality of chart parameters based on the dataset. The one or more computer processors generate a plurality of charts utilizing the determined plurality of chart parameters, selected key-events, associated data, and a timeline generator. The one or more computer processors cluster the generated plurality of charts into a one or more chart macro-clusters. The one or more computer processors decompose the one or more chart macro-clusters into one or more chart micro-clusters.

Abstract: A system, computer program product, and method are presented for classifying behaviors and predictions through processing temporal financial features with a recurrent neural network (RNN). The method includes receiving, by a RNN model, first financial transaction events. The method also includes classifying non-fraudulent behavioral patterns and potentially fraudulent behavioral patterns resident within the first financial transaction events and training the RNN model therewith. The method further includes receiving, by the RNN model, second financial transaction events over a predetermined period of time. The method also includes normalizing the second financial transaction events, including partitioning the predetermined period of time into a plurality of first equal temporal segments. Some of the plurality of first equal temporal segments are representative of the second financial transaction events residing therein.

Abstract: An example operation may include one or more of a computer storing a first set of received data analytics in a blockchain, the first set associated with a subject matter. The operation further comprises the one or more computer storing a second set of received data analytics in the blockchain, the second set associated with the subject matter. The operation further comprises the one or more computer deriving and storing in the blockchain a first set of metrics based on analysis of the first set of analytics processed with the second set of analytics. The operation further comprises the one or more computer storing a third set of received data analytics in the blockchain, the third set associated with the subject matter, and deriving and storing a second set of metrics based on analysis of the first set of metrics processed with the third set of data analytics.

Abstract: The present invention provides an activated carbon modification method, a filter mesh structure, use of the filter mesh structure, and a filter material regeneration method. The activated carbon modification method includes: providing an activated carbon; treating the surface of the activated carbon with hydrogen peroxide, so that the activated carbon forms a modified activated carbon; and removing the hydrogen peroxide from the surface of the modified activated carbon. The filter mesh structure includes the modified activated carbon, and the filter material therein can withstand hydrogen peroxide and temperatures above 100° C. and below 120° C. The filter material regeneration method includes: providing a filter material of the filter mesh structure as described above; treating the filter material with hydrogen peroxide; and removing substances from the surface of the modified activated carbon.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for improving data simulation using reinforcement learning. The method includes one or more processors generating a first simulated data set based on a first parameter set. The method further includes generating a second parameter set, by modifying one or more parameters of the first parameter set, and then generating a second simulated data set based on the second parameter set. The method further includes determining data discrepancies between the first simulated data set and a target data set and determining data discrepancies between the second simulated data set and the target data set. The method further includes selecting between the first and second simulated data sets, a first data set that corresponds to fewer data discrepancies relative to the target, then comparing data discrepancies of the selected first data set to a data discrepancy threshold.

Abstract: A computer-implemented method to automatically identify hotspots in a network graph. The method includes receiving, by a processor, input data, wherein the input data includes a plurality of messages, each message containing a set of message data. The method further includes generating, by a pattern detector, and based on the input data, a network graph, wherein the network graph includes a plurality of nodes. The method also includes determining a first risk indicator for each of the plurality of nodes. The method includes assigning a first weight to the first risk indicator for each of the plurality of nodes. The method further includes identifying a first hotspot in the plurality of nodes, wherein the first hotspot is based on the first weight of the first risk indicator of a first node. The method also includes outputting, by a network interface, the first hotspot and the network graph.

Abstract: An automatic detection method and an automatic detection system for detecting any crack on wafer edges are provided. The automatic detection method includes the following steps. Several wafer images of several wafers are obtained. The wafer images are integrated to create a templet image. Each of the wafer images is compared with the templet image to obtain a differential image. Each of the differential images is binarized. Each of the differential images which are binarized is de-noised. Whether each of the differential images has an edge crack is detected according to pattern of each of the differential images which are de-noised.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can, in response to receiving a request, dynamically determine variables associated with a transaction. Embodiments of the present invention can then generate a historical timeline for a respective target comprising images representing transactions affected by the dynamically determined variables. Embodiments of the present invention can then predict behavioral patterns of the respective target based on the generated historical time.

Abstract: In an approach for evaluating system generated historical transaction timeline images for a computer vision deep learning process, a processor provides one or more chart evaluation factors for evaluating historical timeline images for a deep learning of patterns based on the historical timeline images. A processor generates a quantitative metric based on the one or more chart evaluation factors using a quantitative technique. A processor determines a score for an input timeline image based on the quantitative metric. A processor filters input space based on the score. A processor, in response to receiving a feedback, adjusts a chart setting based on the one or more chart evaluation factors.

Abstract: A non-volatile memory structure including a substrate, a plurality of charge storage layers, a first dielectric layer, and a control gate is provided. The charge storage layers are located on the substrate. An opening is provided between two adjacent charge storage layers. The first dielectric layer is located on the charge storage layers and on a surface of the opening. A bottom cross-sectional profile of the first dielectric layer located in the opening is a profile that is recessed on both sides. The control gate is located on the first dielectric layer and fills the opening.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises determining a pattern within received data based on a periodicity associated with the received data; in response to a calculated signal score associated with the determined pattern of the received data meeting or exceeding a predetermined threshold transaction amount, tracing at least one location associated with the received data; and dynamically suspending an action associated with an account that generated the received data in response to the traced location not being associated with a historical baseline of the account.

Abstract: A method, system, and computer program product for computer vision modeling are provided. The method identifies a set of transactions. A set of categorical behavior transaction types are determined for the set of transactions. The set of categorical behavior transaction types are mapped to a set of colors in a color coordinate system. The method scales color component values of the set of colors in the color coordinate system to generate a pattern of colorized units at intervals along a timespan of the set of transactions. The method generates a fraud detection model based on the set of transactions and the color component values.

Abstract: An approach is provided that receives data sets pertaining to entities. The received data sets are analyzed using a trained artificial intelligence (AI) system, with the analysis results in a fraud probability level. In response to the probability level indicating a high probability of fraud, a rule-based decision tree is applied to the received set of data. A behavior pattern is identified based on a result of the rule-based decision tree and this behavior pattern is used to identify a possible fraudulent event pertaining one of the entities.

Abstract: A semiconductor device includes a substrate having a magnetic random access memory (MRAM) region and a logic region, a first metal interconnection on the MRAM region, a second metal interconnection on the logic region, a stop layer extending from the first metal interconnection to the second metal interconnection, and a magnetic tunneling junction (MTJ) on the first metal interconnection. Preferably, the stop layer on the first metal interconnection and the stop layer on the second metal interconnection have different thicknesses.

Abstract: An example operation may include one or more of a computer deriving a first set of metrics from processing a first and second set of data analytics, the sets associated with a subject matter. The operation further comprises the one or more computer deriving a second set of metrics from processing a third and fourth sets of data analytics, the third and fourth sets associated with the subject matter. The operation further comprises the one or more computer publishing the first and second set of metrics. The operation further comprises the one or more computer receiving a first plurality of requests for processing of analytics using the first set of metrics. The operation further comprises the one or more computer receiving a second plurality of requests for processing of analytics using the second set of metrics. The operation further comprises the one or more computer maintaining tallies of the requests.

Abstract: A method for fabricating semiconductor device includes the steps of forming an inter-metal dielectric (IMD) layer on a substrate, forming a trench in the IMD layer, forming a synthetic antiferromagnetic (SAF) layer in the trench, forming a metal layer on the SAF layer, planarizing the metal layer and the SAF layer to form a metal interconnection, and forming a magnetic tunneling junction (MTJ) on the metal interconnection.