Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A method involves receiving search queries, having search terms, submitted to at least one computerized search engine. For each query, a corresponding pairwise relation in the search queries is calculated. The corresponding pairwise relation is a corresponding probability of a potential edge relationship between at least two terms. Thus, potential edges are formed. A general graph of the terms is constructed by selecting edges from the potential edges. The general graph is nodes representing the terms used in the search queries. The general graph also is edges representing semantic relationships among the nodes. A hierarchical graph is constructed from the general graph by altering at least one of the edges among the nodes in the general graph to form the hierarchical graph.

Abstract: An endoscope camera assembly includes a focus group having one or more lenses. The focus group is movable along a light path. The camera assembly also includes a sensor configured to receive light and to capture an image. The camera assembly further includes a controller configured to determine a region of interest in the image, compute contrast of the region of interest, and move the focus group to optimize contrast of the region of interest.

Abstract: A system for surgical mesh analysis includes a display screen, a processor, and a memory. The memory has instructions stored thereon, which when executed by the processor, cause the system to: access an image of a surgical site (602). The image includes the surgical mesh. The surgical mesh includes a grid of cells. The instructions, when executed by the processor further cause the system to detect a first desired location on the surgical mesh in the image (604); detect a second desired location on the surgical mesh in the image (606); determine a distance between the first desired location and the second desired location along the surgical mesh in the image (608); and display, on the display screen, the determined distance (610).

Abstract: A spatial orientation determining system includes an imaging device configured for obtaining an image of a surgical site, a surgical tool defining a central axis, a processor, and a memory. The surgical tool configured for operating at the surgical site and disposed thereon, a fiducial marker generated by a machine learning network. The fiducial marker includes a distinct pattern. The memory, includes instructions which when executed by the processor, cause the system to: access an image from the imaging device, the image including a portion of the fiducial marker; determine a spatial positioning of the surgical tool based on at least a visible portion of the fiducial marker and the distinct pattern; and determine, based on the spatial positioning, a positioning, an orientation, and/or a rotational angle of the surgical tool.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The selected link is ranked above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.

Abstract: A modified endoinulinase is provided, comprising modified wild-type T. purpuregenus endoinulinase, or a functional fragment thereof, in which an amino acid residue at each one of one or more positions corresponding to 128, 316, 344, 350 or 504 of wild-type T. purpuregenus endoinulinase is substituted, wherein: (i) a tyrosine residue corresponding to Y128 is substituted with H, K or R; a glutamate residue corresponding to E344 is substituted with K, H or R; and a threonine residue corresponding to T504 is substituted with M, S or Y; and optionally an alanine residue corresponding to A316 is substituted with T, S, C or M; (ii) a tyrosine residue corresponding to Y128 is substituted with H, K or R; a glutamate residue corresponding to E344 is substituted with K, H or R; a threonine residue corresponding to T504 is substituted with M, S or Y; and a glutamine residue corresponding to Q350 is substituted with L, G, A, V or I; or (iii) a tyrosine residue corresponding to Y128 is substituted with H, K or R.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: A system and method for optimizing a defense model using available security capabilities are provided. The method includes obtaining a defense model and an optimal security application implementation associated with the defense model; evaluating available security capabilities deployed in an enterprise environment to determine a plurality of variant security applications implementing the defense model; determining a quality score for each of the plurality of the variant security applications; selecting, from the plurality of variant security applications, a variant security application having a highest quality score; and executing the selected variant security application.

Abstract: A system and method for optimizing a defense model using available security capabilities are provided. The method includes obtaining a defense model and an optimal security application implementation associated with the defense model; evaluating available security capabilities deployed in an enterprise environment to determine a plurality of variant security applications implementing the defense model; determining a quality score for each of the plurality of the variant security applications; selecting, from the plurality of variant security applications, a variant security application having a highest quality score; and executing the selected variant security application.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: Machines, systems and methods for managing quality of service (QoS) in a multi-tenant virtualized computing environment, the method comprising: collecting transmission rate statistics associated with data communicated in a virtual network, wherein at least one virtual switch monitors communications initiated by at least a virtual machine (VM) executed on a host machine serviced by the virtual switch; determining, by way of the virtual switch, profile parameters associated with a first communication initiated by the VM belonging to at least a first group, wherein a connection request is submitted by the virtual switch to a traffic controller to assist in establishing the first communication; classifying the connection request for establishing the first communication according to the profile parameters associated with the first communication; determining a first aggregated transmission rate associated with the VM that initiated the first communication based on the classifying.

Abstract: A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The selected link is ranked above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.

Abstract: A method and system for classification of cyber-threats is provided. The method includes receiving a request for classifying a cyber-threat detected by a cyber-security system, wherein the request includes initial information about the detected cyber-threat; enriching the initial information about the detected cyber-threat to provide textual information about at least one perceived threat related to the detected cyber-threat; and classifying each of the at least one perceived threat into a security service, wherein the classification is performed based on the respective textual information.

Abstract: A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on a rank engine and the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The rank engine ranks the selected link above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.

Abstract: A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on a rank engine and the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The rank engine ranks the selected link above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.

Abstract: A modified endoinulinase is provided, comprising modified wild-type T. purpuregenus endoinulinase, or a functional fragment thereof, in which an amino acid residue at each one of one or more positions corresponding to 128, 316, 344, 350 or 504 of wild-type T. purpuregenus endoinulinase is substituted, wherein: (i) a tyrosine residue corresponding to Y128 is substituted with H, K or R; a glutamate residue corresponding to E344 is substituted with K, H or R; and a threonine residue corresponding to T504 is substituted with M, S or Y; and optionally an alanine residue corresponding to A316 is substituted with T, S, C or M; (ii) a tyrosine residue corresponding to Y128 is substituted with H, K or R; a glutamate residue corresponding to E344 is substituted with K, H or R; a threonine residue corresponding to T504 is substituted with M, S or Y; and a glutamine residue corresponding to Q350 is substituted with L, G, A, V or I; or (iii) a tyrosine residue corresponding to Y128 is substituted with H, K or R.

Abstract: Aspects of the present disclosure provide techniques for predicting content relevant to questions based on reference links. Embodiments include receiving a set of question and answer (Q/A) pairs and identifying a set of references in the set of Q/A pairs that link pairs of Q/A pairs of the set of Q/A pairs. Embodiments include identifying popular Q/A pairs of the set of Q/A pairs based on the set of references. The popular Q/A pairs may be referenced by a subset of the set of Q/A pairs and each respective Q/A pair of the subset of the set of Q/A pairs may comprise a respective question of a plurality of questions. Embodiments include training a model based on the plurality of questions, the popular Q/A pairs, and the set of references, to predict Q/A pairs of the set of Q/A pairs that are relevant to a given question.