Abstract: Techniques described herein relate to a method for identifying offline accounts for online adoption targeting, including obtaining an account group data set; generating, using the account group data set, an enhanced account group data set comprising the account group data set and a derived data item; obtaining, using a ML model and the enhanced account group data set, a key metric set; performing a clustering analysis using the key metric set and a portion of the enhanced account group data set to obtain account clusters; assigning a weight to a cluster of the account clusters; performing a similarity computation for an offline account of the account group using the key metric set and the portion of the enhanced account group data set to obtain a weighted similarity score; adding the weighted similarity score to a ranked list of weighted similarity scores; and providing the ranked list to an interested entity.

Abstract: Techniques are provided for sentiment-based adaptations of user representations in virtual environments. One method comprises obtaining information characterizing a first user and a second user interacting with a session of a virtual environment; applying the information to an analytics engine to obtain a sentiment status indicating a sentiment of the second user in the virtual environment, wherein the analytics engine processes the obtained sentiment status of the second user to select an adaptation of a facial expression and/or a body language of a representation of the first user from learned sentiment-based facial expression examples and/or learned sentiment-based body language examples, using the obtained sentiment status of the second user; and automatically initiating a rendering of the virtual environment using the selected adaptation of the facial expression and/or the body language of the representation of the first user in the virtual environment.

Abstract: An information handling system may receive a plurality of rules and a file for malware testing. The information handling system may apply two or more of the plurality of rules to the received file to determine a plurality of outcomes of application of the rules to the file. The information handling system may determine whether to classify the received file as malware or not malware by applying a machine learning model to the plurality of outcomes.

Abstract: Techniques are provided for estimating a delivery time for a product in a supply chain. One method comprises obtaining an order for at least one product; calculating a similarity-based feature and/or a proximity-based feature for the order; and applying the calculated similarity-based feature and/or the calculated proximity-based feature for the order to a machine learning engine that generates an estimated delivery time for the order, wherein the machine learning engine is trained using characteristics from historical orders. The similarity-based feature for the order can be calculated using a delivery time value of historical orders in a given order cluster where the order was assigned based on a predefined distance metric between the order and features of each order cluster. The proximity-based feature for the order can be calculated using a delivery time value of the historical orders that satisfy one or more predefined recency criteria.

Abstract: Techniques are provided for software code refactoring prioritization using software code defect aggregation in a graphical code representation. One method comprises obtaining an indication of an error in software code; obtaining a graphical representation of the software code, wherein each node in the graphical representation corresponds to a function and wherein nodes are connected based on a hierarchical relationship; assigning a weight to a first node that corresponds to a function associated with the error; assigning a decayed weight to an additional node, selected based on the hierarchical relationship with the first node, wherein the decayed weight is determined by applying a decay function to the weight assigned to the first node; prioritizing nodes in the graphical representation for a code refactoring of the software code based on the weights assigned to each node; and initiating an automated action based on a result of the prioritizing.

Abstract: Techniques are provided for anomaly-based ransomware detection of encrypted files. One exemplary method comprises obtaining metadata for an encrypted file; applying an anomaly detection technique to the metadata to compare at least one attribute in the metadata to one or more corresponding historical baseline values for the at least one attribute; and determining whether the encrypted file comprises a ransomware encryption based on the comparison. In some embodiments, one or more of file extension attributes, file size attributes and file name attributes in the metadata are compared to the one or more corresponding historical baseline values to identify a ransomware attack.

Abstract: A method, comprising: receiving a first search query that is associated with a video file; retrieving one or more search results in response to the first search query, each of the search results corresponding to a different section in the video file; and displaying the search results on a display device, wherein displaying any of the search results includes displaying a link that points to the section of the video file, which corresponds to the search result.

Abstract: Techniques described herein relate to a method for identifying offline accounts for online adoption targeting, including obtaining an account group data set; generating, using the account group data set, an enhanced account group data set comprising the account group data set and a derived data item; obtaining, using a ML model and the enhanced account group data set, a key metric set; performing a clustering analysis using the key metric set and a portion of the enhanced account group data set to obtain account clusters; assigning a weight to a cluster of the account clusters; performing a similarity computation for an offline account of the account group using the key metric set and the portion of the enhanced account group data set to obtain a weighted similarity score; adding the weighted similarity score to a ranked list of weighted similarity scores; and providing the ranked list to an interested entity.

Abstract: A method, comprising: receiving a first search query that is associated with a video file; retrieving one or more search results in response to the first search query, each of the search results corresponding to a different section in the video file; and displaying the search results on a display device, wherein displaying any of the search results includes displaying a link that points to the section of the video file, which corresponds to the search result.

Abstract: One example method includes detecting grey market orders with a detection model. Data from historical orders, which include confirmed grey market orders, can be clustered based on engineered features of the data such that similar orders are clustered together. A new order can be assigned to one of the clusters and a similarity score of the new order to the orders in the assigned cluster can be generated. The score reflects the likelihood that the new order is a grey market order. Action can be taken on the new order based on the score.

Abstract: One example method includes generating a searchable video library. Video files are processed to extract text corresponding to the speech and to the images. The extracted text is semantically searched such that specific portions or locations of video files can be identified and returned in response to a query.

Abstract: One example method includes generating personalized content for online sales. Recorded calls and other sources are converted to text. The text is semantically processed and correlated with customer data including customer order data to identify content that impacted the purchase of a product. The content may be incorporated into an online product page to facilitate online sales.

Abstract: Techniques described herein relate to a method for identifying breakpoints. The method may include obtaining, by a breakpoint identification device, an entity group data set corresponding to an entity group; generating, using the entity group data set, an enhanced entity group data set comprising the entity group data set and a derived data item; performing a clustering analysis using at least a portion of the enhanced entity group data set to obtain a plurality of entity clusters; training a machine learning (ML) model using the enhanced entity group data set to obtain a trained ML model; using the trained ML model and a set of simulated entities to perform a breakpoint analysis; generating a breakpoint graph based on the breakpoint analysis; and providing recommendations to an interested entity based on a breakpoint identified using the breakpoint graph.

Abstract: Techniques are provided for basic input/output system (BIOS) protection using multi-factor authentication (MFA) based on digital identity values. One method comprises obtaining, by a BIOS of a hardware device, from a user device, (i) a request to access the BIOS, and (ii) a token based on a digital identity value for the user device; providing the token to an MFA chip on the hardware device, wherein the MFA chip evaluates the token and provides a verification result to the BIOS; and allowing the user device to access the BIOS based on the verification result. The digital identity value for the user device may be stored by the MFA chip during a fabrication of the MFA chip and/or a registration of the user device. The MFA chip may compare the digital identity value from the token received from the BIOS with the digital identity value for the user device stored by the MFA chip.

Abstract: One example method includes detecting grey market orders with a detection model. Data from historical orders, which include confirmed grey market orders, can be clustered based on engineered features of the data. A new order can be assigned to one of the clusters based on similarity and a score for the new order can be generated that reflects the likelihood that the new order is a grey market order. Action can be taken on the new order based on the score. The scores output by the detection model can be reviewed such that user input regarding the scores can be used to retrain the detection model.

Abstract: In one embodiment, a method for verifying a user of an information handling system that includes: identifying, by a verification manager of an information handling system of a computing environment, one or more components of the information handling system; generating, by the verification manager, a static indicator associated with the information handling system based on the one or more components; verifying, by the verification manager, the static indicator; identifying, by the verification manager, the user of the information handling system; identifying, by the verification manager, a plurality of behavioral characteristics associated with the user; generating, by the verification manager, a dynamic indicator associated with the user based on the plurality of behavioral characteristics; verifying, by the verification manager, the dynamic indicator; determining, by the verification manager, a user accessibility associated with the information handling system based on the static indicator and the dynamic in

Abstract: Techniques are provided for detecting malicious software code embedded in image files, using machine learning. One method comprises obtaining metadata for an image file; applying the obtained metadata to at least one machine learning technique to classify the image file into at least one of a plurality of predefined classes, wherein the plurality of predefined classes comprises at least one malicious file class; and determining whether the image file comprises malicious software code based on the classification. The machine learning technique can be trained using image files classified into at least one of the plurality of predefined classes. The machine learning technique may employ a deep neural network and/or a convolutional neural network to classify the image file into the at least one predefined class.

Abstract: A customer blockchain data store is provided. An exemplary method comprises obtaining a blockchain associated with a given customer of an enterprise having multiple customer communication channels, wherein the blockchain comprises transaction data for the given customer with the customer communication channels; obtaining new transaction data for the given customer for a given one of the customer communication channels; providing the new transaction data for the given customer to additional customer communication channels; receiving a validation of the new transaction data from the additional customer communication channels based on one or more predefined validation criteria; and storing the validated new transaction data for the given customer in the blockchain associated with the given customer.

Abstract: Techniques are provided for firmware protection using multi-chip storage of firmware images. One method comprises obtaining a firmware image; encrypting the firmware image; splitting the encrypted firmware image into a plurality of encrypted firmware image portions; and storing the plurality of encrypted firmware image portions on a plurality of recovery chips, wherein a threshold number of the encrypted firmware image portions from at least two different recovery chips are needed to reconstruct the firmware image. The threshold number of the encrypted firmware image portions can be obtained from the at least two different recovery chips and a validation can be applied to the obtained encrypted firmware image portions. The threshold number of encrypted firmware image portions may be obtained in response to a chip that stores the firmware image being inactive.

Abstract: Techniques are provided for basic input/output system (BIOS) protection using multi-factor authentication (MFA) based on digital identity values. One method comprises obtaining, by a BIOS of a hardware device, from a user device, (i) a request to access the BIOS, and (ii) a token based on a digital identity value for the user device; providing the token to an MFA chip on the hardware device, wherein the MFA chip evaluates the token and provides a verification result to the BIOS; and allowing the user device to access the BIOS based on the verification result. The digital identity value for the user device may be stored by the MFA chip during a fabrication of the MFA chip and/or a registration of the user device. The MFA chip may compare the digital identity value from the token received from the BIOS with the digital identity value for the user device stored by the MFA chip.