Abstract: In a quality control method applied in manufacturing, product information of a product is obtained. Manufacturing parameters corresponding to the product information are queried. The manufacturing parameters are input into a product quality prediction model which is trained to obtain the value of at least one quality inspection of each product. If such quality inspection value is not equal to a standard value or is not within a standard value range, an incorrect manufacturing parameter is identified from all the manufacturing parameters applicable to each product, the incorrect manufacturing parameter being output when identified.

Abstract: An isolated antigen-binding protein, having one or more of the following properties: 1) capable of binding to human and monkey-derived GITR proteins at a KD value of 7×10?12 or below, wherein the KD value is measured by BLI method; 2) capable of stimulating immune cell proliferation; 3) capable of stimulating immune cells to secrete IFN-?, wherein the secretion is measured in T cell viability assay; 4) capable of inhibiting tumor growth and/or tumor cell proliferation; 5) capable of activating GITR signaling pathway; 6) capable of inhibiting the binding of GITR to GITRL.

Abstract: A processing method of an adaptation layer of an integrated access and backhaul node and an adaptation layer are provided. The method includes: mapping, by the adaptation layer of the integrated access and backhaul node, a received first data packet to a first bearer or channel between the integrated access and backhaul node and a first node; transmitting, by the adaptation layer of the integrated access and backhaul node, the first data packet to the first node; wherein the first node is a downstream integrated access and backhaul node relative to the integrated access and backhaul node and/or a UE accessing the integrated access and backhaul node, or the first node is an upstream integrated access and backhaul node relative to the integrated access and backhaul node or a donor node of the integrated access and backhaul node.

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: Technical solutions are described for improving the performance of natural language processing systems and other such human-computer interaction systems by facilitating analyzing unstructured computer text by converting such unstructured computer text to domain-specific groups using network graphs. The technical solutions use a graph to connect similar terms with attributes and structural information to facilitate the grouping of different terms that may be used to describe the same entity. Technical solutions facilitate analyzing different input data to generate a graph that can be further used to find data similarity in the input data. The generated graph captures attributes associated with each term and assigns groupings for all the terms at the same time, improving the performance of the natural language processing (NLP) system that is analyzing the input data.

Abstract: A method, computer system, and a computer program product for labeling optimization is provided. The present invention may include receiving a plurality of labeled historical transaction timeline image clusters based on a plurality of historical transaction timeline images clustered using unsupervised machine learning. The present invention may further include training an image recognition model using supervised machine learning based on the received plurality of labeled historical transaction timeline image clusters. The present invention may also include receiving, by the trained image recognition model, a current transaction timeline image. The present invention may further include assigning a corresponding label to the received current transaction timeline image based on matching the received current transaction timeline image to one of the received plurality of labeled historical transaction timeline image clusters.

Abstract: Disclosed is a catalyst for preparing 2,3,3,3-tetrafluoropropene by gas-phase hydrodechlorination, which solves the problem of the high costs and easy deactivation of traditional chlorofluorocarbon hydrodechlorination catalysts. The disclosed catalyst is characterized in consisting of an active component and a carrier, wherein the active component is a combination of one or more of the metals: Ni, Mo, W, Co, Cr, Cu, Ce, La, Mn and Fe. The catalyst in the present invention has excellent performance, high activity, good stability and a low reaction temperature, effectively reduces reaction energy consumption, and has industrial application value.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises receiving transactional data from at least two users in a plurality of users; determining a pattern within the received transactional data based on a frequency-based domain conversion, wherein the pattern is associated with a determined periodicity; determining a delay within the received transactional data by identifying contextual factors associated with the received transactional data and measuring an amount of time between each identified contextual factors within a plurality of identified contextual factors using a signal processing algorithm; aligning the received transactional data from the at least two users by placing at least two signal forms associated based on the determined delay within the received transactional data at a same point; and generating a line graph depicting the aligned transactional data.

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: The present disclosure provides proteinaceous complexes, pharmaceutical compositions, medicaments and/or kits comprising the proteinaceous complexes, methods for producing the proteinaceous complexes, and uses thereof.

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: 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: 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.