Abstract: Embodiments provide a computer implemented method in a data processing system comprising a processor and a memory comprising instructions, which are executed by the processor to cause the processor to implement the method of removing a cognitive terminology from a news article at a news portal, the method including: receiving, by the processor, a first news article from a user; configuring, by the processor, a cognitive terminology filter list to add one or more entities and one or more cognitive terminology types associated with each entity in the cognitive terminology filter list; dividing, by the processor, the first news article into a plurality of text segments; identifying, by the processor, one or more key entities and one or more inter-entity relationships of each text segment; detecting, by the processor, one or more cognitive terminologies in the first news article; and providing, by the processor, one or more suggestions to remove the one or more cognitive terminologies.

Abstract: One embodiment provides a method, including: receiving, from an application, information for storage in a storage management system, wherein the storage management system comprises a plurality of storage layers, each storage layer having a different performance and a different cost than the other storage layers; labeling the information with one of a plurality of labels relevant to the application; assigning the information a performance tolerance value based upon the label of the information, wherein the performance tolerance value comprises an estimate of the performance requirement required by the application storing the information; determining a storage layer for storage of the information, wherein the determining comprises identifying one of the plurality of storage layers corresponding to the label of the information and updating metadata of a logical volume corresponding to the information with the performance tolerance value; and sending the information to the determined storage layer.

Abstract: One embodiment provides a method, including: deploying a machine learning model, wherein the deployed machine learning model is used in responding to queries from users; receiving, at the deployed machine learning model, input from a user; identifying a type of machine learning model attack corresponding to the received input; computing, responsive to receiving the input, a resiliency score of the machine learning model, wherein the resiliency score indicates resistance of the machine learning model against the identified type of attack; and performing an action responsive to the computed resiliency score.

Abstract: One embodiment provides a method, including: receiving, from an application, information for storage in a storage management system, wherein the storage management system comprises a plurality of storage layers, each storage layer having a different performance and a different cost than the other storage layers; labeling the information with one of a plurality of labels relevant to the application; assigning the information a performance tolerance value based upon the label of the information, wherein the performance tolerance value comprises an estimate of the performance requirement required by the application storing the information; determining a storage layer for storage of the information, wherein the determining comprises identifying one of the plurality of storage layers corresponding to the label of the information and updating metadata of a logical volume corresponding to the information with the performance tolerance value; and sending the information to the determined storage layer.

Abstract: One embodiment provides a method, including: deploying a machine learning model, wherein the machine learning model is used in responding to queries from users; receiving, at the deployed machine learning model, input from at least one entity; determining that the at least one entity is an adversary attempting to retrain and/or steal the deployed machine learning model; and providing, in view of the determining that the at least one entity is an adversary, an altered response, wherein the altered response comprises at least one of: a response from a machine learning model other than the deployed machine learning model and a response from the deployed machine learning model altered with errors.

Abstract: An apparatus for dynamically debiasing an online job application system includes a processor and a memory that stores code executable by the processor to receive a plurality of job listings and corresponding job descriptions in response to a search query on an online job listing system and to dynamically modify bias terms of the job description for each of the job listings based on profile information for a user such that each of the job descriptions conforms to the user's profile information. The apparatus includes code executable by the processor to dynamically rank each of the job listings based on the modified job descriptions and with respect to the user's profile information and the search query and to present the job listings and their corresponding modified job descriptions in order of the rank for each of the job listings.

Abstract: One embodiment provides a method, including: obtaining at least one video into which an advertisement is to be inserted; selecting an advertisement for insertion into the at least one video, wherein the selecting comprises (i) analyzing the at least one video to identify context of the at least one video and (ii) selecting an advertisement that is related to the context of the at least one video; determining a location within the at least one video for insertion of the advertisement, wherein the determining a location comprises (i) identifying criticality of a scene within the video where the advertisement is to be inserted and (ii) selecting the location based upon the criticality of the scene; and inserting the advertisement within the at least one video at the determined location.

Abstract: Techniques facilitating autonomously rendering an encrypted data anonymous in a non-trusted environment are provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a plurality of clusters of encrypted data from an encrypted dataset using a machine learning algorithm. The computer-implemented method can also comprise modifying, by the system, the plurality of clusters based on a defined criterion that can facilitate anonymity of the encrypted data.

Abstract: Techniques facilitating autonomously rendering an encrypted data anonymous in a non-trusted environment are provided. In one example, a computer-implemented method can comprise generating, by a system operatively coupled to a processor, a plurality of clusters of encrypted data from an encrypted dataset using a machine learning algorithm. The computer-implemented method can also comprise modifying, by the system, the plurality of clusters based on a defined criterion that can facilitate anonymity of the encrypted data.

Abstract: Methods, systems, and computer program products for enabling distance-based algorithms on data encrypted using a 2DNF homomorphic encryption scheme with inefficient decryption are provided herein. A computer-implemented method includes generating multiple versions of a data point, wherein each of the multiple versions of the data point comprises a distinct value corresponding to a distinct Euclidean space; encrypting each of the multiple versions of the data point; storing the multiple encrypted versions of the data point across multiple databases; and executing one or more distance-based algorithms on the multiple encrypted versions of the data point by using a finite decryption table across the multiple databases, wherein the finite decryption table stores a set of plaintext-ciphertext mappings between (i) multiple plaintext values and (ii) multiple encrypted ciphertext values corresponding to the multiple plaintext values.

Abstract: A method, a system, and a computer program product are provided for analyzing an instructional video. Video data of an instructional video is analyzed to form multiple units of work. Each unit of work is a respective grouping of video frames of the instructional video based on a respective logical combination of activities associated therewith. Each unit of work is analyzed to produce a respective action graph of activities included in the unit of work, the respective action graph indicating interdependencies among the activities included therein. Interdependencies among activities across the units of work are determined to form a critical path graph. A received query is processed to provide troubleshooting assistance with respect to the instructional video based on the units of work, the action graphs, the critical path graph, and a knowledge base including information related to a subject matter of the instructional video.

Abstract: Methods, systems and computer program products for root cause analysis using provenance data are provided herein. A computer-implemented method comprises computing a plurality of provenance paths for at least one of a plurality of data elements in a curation flow and a plurality of groups of data elements in the curation flow, analyzing the computed provenance paths to determine one or more errors in the curation flow, and outputting the one or more errors in the curation flow to at least one user. The analyzing comprises at least one of identifying which of the computed provenance paths are partial provenance paths, and identifying one or more output records associated with the curation flow, wherein the one or more output records comprise incorrectly curated data, and identifying the computed provenance paths that respectively correspond to the one or more output records comprising the incorrectly curated data.

Abstract: Methods, systems, and computer program products for generating collaborative orderings of information pertaining to products to present to target users are provided herein.

Abstract: One embodiment provides a method, including: receiving, at a third-party storage provider and from a data owner, a plurality of encrypted documents, wherein each of the plurality of encrypted documents is encrypted by the data owner using at least one encryption key; receiving, from a query user, an encrypted query, wherein the query is encrypted using the at least one encryption key; computing an edit distance value between the encrypted query and at least a portion of the plurality of encrypted documents, wherein the computing comprises communicating with an entity to work together to compute the edit distance value; the communicating comprising (i) providing, from the third-party storage provider to the entity, an encrypted function of an edit distance matrix and (ii) receiving an encrypted edit distance value computed by the entity from the encrypted function; and returning the encrypted edit distance value to the query user.

Abstract: One embodiment provides a method, including: receiving a query from a user requesting assistance regarding instructions for performing a task; identifying, within steps of the instructions, words that can be visualized, wherein the identifying comprises identifying relationships between terms within the query to generate a step query; retrieving, for each of the steps, a plurality of images representing the identified words; identifying at least one object occurring within the plurality of images corresponding to more than one of the steps; selecting an image for each of the steps of the instructions, wherein the selecting an image comprises selecting an image for each step such that the identified at least one object is represented similarly in each selected image including the identified at least one object; and presenting the instructions as visualized instructions by presenting the selected images for each of the steps in order.

Abstract: Methods, systems, and computer program products for assessing value of one or more data sets in the context of a set of applications are provided herein. A computer-implemented method includes selecting analytic applications of interest based on a characterization of data attributes of each of the available data sets; automatically determining an impact of each of the data attributes of each of the available data sets on an end value of each of the analytic applications of interest; automatically computing an amount of improvement to the end value of each of the analytic applications of interest based on inclusion of an additional data set; and automatically determining a value attributed to the additional data set based on a comparison of (i) the cost of adding the additional data set to the available data sets to (ii) the computed amount of improvement based on the inclusion of the additional data set.

Abstract: Asset recommendation for a particular input dataset is provided. Candidate data analysis assets having a corresponding relatedness score associated with the particular input dataset greater than a defined relatedness score threshold value are selected. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are ranked by score. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are listed by rank from highest to lowest. A justification for each candidate data analysis asset is inserted in the ranked list of candidate data analysis assets. The ranked list of candidate data analysis assets along with each respective justification is outputted on a display device.

Abstract: A processor may receive plaintext data. The plaintext data may correspond to a query. The processor may identify a granularity of the plaintext data. The processor may compress the plaintext data using a binary search tree. The binary search tree may compress the plaintext data based on the granularity of the plaintext data. The processor may encrypt the plaintext data by randomizing the order in which the binary search tree stores the compression of the plaintext data. The stored order of the binary search tree may act as a private key. The processor may process the query over an encrypted cumulative compressed database.

Abstract: Methods, systems, and computer program products for tracking missing data using provenance traces and data simulation are provided herein. A computer-implemented method includes generating, for each of multiple stages in a data curation sequence, a machine learning model of the data curation sequence, wherein the model is based on historical input records within the data curation sequence, historical output records within the data curation sequence, and provenance data within the data curation sequence; creating a simulated output record based on a detected anomaly corresponding to the data curation sequence; predicting the content of absent input records that precede the simulated output record in the data curation sequence and provenance data corresponding to the simulated output record; and outputting, to a user, in response to a query pertaining to the detected anomaly, the predicted input records and information relating the predicted input records to the detected anomaly.

Abstract: Methods, systems and computer program products for association rule mining of an encrypted database are provided herein. A computer-implemented method includes receiving, at a first cloud computing environment, encrypted transaction data that are encrypted using an encryption scheme which provides additive homomorphism, wherein the transaction data comprise a plurality of combinations of two or more elements of a set of elements, receiving, at the first cloud computing environment, encrypted query data that are encrypted using the encryption scheme, wherein the query data comprise at least one of an element and a combination of two or more elements of the set of elements which are the subject of a query seeking a determination of whether at least one of the element and the combination of two or more elements is frequent, and computing addition of the encrypted query data with the encrypted transaction data.