Abstract: An apparatus comprises a processing device configured to receive a service request for a given computing device, the service request comprising a given image of a given computing device, to generate a given image embedding for the given image utilizing a machine learning model, and to determine similarity measures between the given image embedding and other image embeddings for other images of computing devices utilizing an angular similarity metric. The processing device is configured to identify whether the given image exhibits at least a threshold level of similarity to at least one other image based at least in part on the determined similarity measures, to classify the given image as potentially fraudulent responsive to identifying the given image as exhibiting at least the threshold level of similarity to at least one other image, and to modify processing of the service request responsive to classifying the given image as potentially fraudulent.

Abstract: An apparatus includes a processing device configured to obtain first and second sets of data records, each data record comprising a string associated with an attribute. The processing device is also configured to generate a similarity matrix, wherein entries of the similarity matrix comprise values characterizing similarity between respective pairs of the strings comprising a first string from a data record in the first set and a second string from a data record in the second set. The processing device is further configured to construct a graph network based on the similarity matrix comprising edges connecting pairs of the data records based on values of entries in the similarity matrix, perform a clustering operation on the graph network to identify clusters, and to initiate remedial action responsive to identifying a given cluster comprising at least one data record from each of the first and second sets of data records.

Abstract: An apparatus includes a processing device configured to obtain data records associated with an enterprise system comprising strings associated with an attribute. The processing device is also configured to generate a similarity matrix with entries comprising values characterizing similarity between respective pairs of the strings. The processing device is further configured to apply a thresholding filter to values in the entries of the similarity matrix to create an adjacency matrix, and to construct a graph network of the data records based at least in part on the adjacency matrix, wherein the graph network comprises edges connecting pairs of the data records. The processing device is further configured to perform a clustering operation on the graph network to identify clusters of the data records for the attribute, and to initiate remedial action in the enterprise system responsive to identifying a given cluster comprising a given subset of the data records.

Abstract: An apparatus comprises a processing device configured to receive a service request for a given computing device, the service request comprising a given image of a given computing device, to generate a given image embedding for the given image utilizing a machine learning model, and to determine similarity measures between the given image embedding and other image embeddings for other images of computing devices utilizing an angular similarity metric. The processing device is configured to identify whether the given image exhibits at least a threshold level of similarity to at least one other image based at least in part on the determined similarity measures, to classify the given image as potentially fraudulent responsive to identifying the given image as exhibiting at least the threshold level of similarity to at least one other image, and to modify processing of the service request responsive to classifying the given image as potentially fraudulent.

Abstract: An apparatus comprises a processing device configured to obtain a time series dataset having missing values, the time series dataset comprising monitoring data associated with one or more assets. The processing device is also configured to generate, utilizing a machine learning algorithm, a reconstructed time series dataset having imputed values for the missing values in the obtained time series dataset, the machine learning algorithm comprising a generative network implementing inverse network parameter determination for network parameters of the generative network. The processing device is further configured to classify patterns in the obtained time series dataset utilizing the reconstructed time series dataset, to select remedial actions for controlling at least one of the one or more assets based at least in part on the classified patterns in the obtained time series dataset, and to initiate the selected remedial actions to control the at least one asset.

Abstract: An apparatus includes a processing device configured to obtain first and second sets of data records, each data record comprising a string associated with an attribute. The processing device is also configured to generate a similarity matrix, wherein entries of the similarity matrix comprise values characterizing similarity between respective pairs of the strings comprising a first string from a data record in the first set and a second string from a data record in the second set. The processing device is further configured to construct a graph network based on the similarity matrix comprising edges connecting pairs of the data records based on values of entries in the similarity matrix, perform a clustering operation on the graph network to identify clusters, and to initiate remedial action responsive to identifying a given cluster comprising at least one data record from each of the first and second sets of data records.

Abstract: An apparatus includes a processing device configured to obtain data records associated with an enterprise system comprising strings associated with an attribute. The processing device is also configured to generate a similarity matrix with entries comprising values characterizing similarity between respective pairs of the strings. The processing device is further configured to apply a thresholding filter to values in the entries of the similarity matrix to create an adjacency matrix, and to construct a graph network of the data records based at least in part on the adjacency matrix, wherein the graph network comprises edges connecting pairs of the data records. The processing device is further configured to perform a clustering operation on the graph network to identify clusters of the data records for the attribute, and to initiate remedial action in the enterprise system responsive to identifying a given cluster comprising a given subset of the data records.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein m, n, p, q A, B, R1, R2, R3, R4, R5, R6 and R7 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein m, n, A, R1, R2, R3, R4 and R5 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein m, n, p, q A, B, R1, R2, R3, R4, R5, R6 and R7 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein m, A, X, Y, Z, R1, R2, R3, R4 and R5 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of the formula I: or pharmaceutically acceptable salts thereof, wherein m, n, p, q, r, X1, X2, X3, X4, Y, Z, A, Het, R1, R2, R3, R4, R5 R6, R7, R8, R9, R10, R11, R12 and R13 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis, muscular sclerosis and psoriasis.

Abstract: Compounds of the formula I: or pharmaceutically acceptable salts thereof, wherein m, n, p, q, r, X1, X2, X3, X4, Y, Z, A, R1, R2, R3, R4, R5 R6, R7, R8, R9, R10, R11, R12 and R13 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis, muscular sclerosis and psoriasis.

Abstract: Compounds of the formula I: or a pharmaceutical salt thereof, wherein p, s, A, R3 and Re are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein m, n, A, R1, R2, R3, R4 and R5 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein R1, R2, R3 and R4 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of the formula I: or a pharmaceutical salt thereof, wherein X, Ar1, R1, R2, R3, R4, R5, R6 and R7 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of the formula I: or pharmaceutically acceptable salts thereof, wherein m, n, p, q, r, X1, X2, X3, X4, Y, Z, A, Het, R1, R2, R3, R4, R5 R6, R7, R8, R9, R10, R11, R12 and R13 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis, muscular sclerosis and psoriasis.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein m, A, X, Y, Z, R1, R2, R3, R4 and R5are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.

Abstract: Compounds of formula I: or pharmaceutical salts thereof, wherein R1, R2, R3 and R4 are as defined herein. Also disclosed are methods of making the compounds and using the compounds for treatment of inflammatory diseases such as arthritis.