Abstract: Implementations include receiving flow data representative of communication traffic of the network, determining that at least one blacklisted Internet protocol (IP) address is present in the flow data, and in response: providing a set of high-dimensional flow representations of network traffic by processing historical flow data through a deep learning (DL) model, providing a set of low-dimensional flow representations of the network traffic based on the set of high-dimensional flow representations, and labeling at least a portion of the set of low-dimensional flow representations to provide a sub-set of labeled low-dimensional flow representations and a sub-set of unlabeled low-dimensional flow representations, and identifying a host associated with an unlabeled low-dimensional flow representation as a potentially malicious host, and in response, automatically executing a remedial action with respect to the potentially malicious host.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support activity recognition using image or video processing (e.g., computer vision) and machine learning. To illustrate, a monitoring device may receive video data from a camera configured to capture video of a scene that includes a pipe of an oil rig. The monitoring device may perform motion detection on the video data to identify frames of the video data depicting motion. The identified frames may be provided to one or more machine learning models that are configured to determine whether the pipe is moving based on input video clips. The one or more machine learning models may be trained using labeled video clips that depict movement of the pipe or lack thereof. The monitoring device may generate an output based on the determination, the output indicating a slip to slip time associated with the pipe.

Abstract: Implementations include evaluating a first sub-set of rules based on a first sub-set of facts to provide a first set of impacts, evaluating including applying the first sub-set of facts to each rule using a hash join operation to determine whether a rule results in an impact, indexes of arguments of facts being used in a probe phase of the hash join operation, evaluating a second sub-set of rules using impacts of the first set of impacts to provide a second set of impacts, determining whether each goal in a set of goals has been achieved using the first set of impacts and the second set of impacts, each goal being provided as an impact, in response to determining that each goal in the set of goals has been achieved, removing paths of the AAG, each of the paths resulting in an impact that is not a goal.

Abstract: An infection risk prediction system may receive a query associated with an infection risk assessment for a geographic location for an infectious disease and obtain associated infectious disease data. The system detects a plurality of objects at the geographical location and a plurality of material categories for the plurality of objects associated. The system determines a usage frequency indicator and spatial-temporal features for each object and creates a risk prediction model based on the lifespan of the virus on the plurality of material categories for each of the plurality of objects based on the viral disease data and the spatial-temporal features for each object. A risk assessment score for each object is determined and an associated heat map is provided to alert a user of potential virus exposures or to provide a routing application to generate a route that minimizes potential pathogen exposure.

Abstract: Implementations of the present disclosure include providing graph data defining a graph that is representative of an enterprise network, the graph including nodes and edges between nodes, each node representing an asset within the enterprise network, and each edge representing one or more lateral attack paths between assets in the enterprise network, determining, for each node, an incoming value based on attributes of a set of incoming edges and an outgoing value based on attributes of a set of outgoing edges, the attributes including a number of edges and semantic types of the edges, at least one cardinality value of each node being determined based on one or more of the incoming value and the outgoing value of the node, receiving input representative of filter parameters, generating a sub-graph based on attributes of the nodes and the filter parameters, and displaying, by the visualization platform, the sub-graph in a display.

Abstract: A computer-implemented method for obtaining content of a document is provided. The method includes: receiving data in an unknown format obtained by an OCR application from the document, the data comprising a plurality of visual elements; for each of the plurality of visual elements, obtaining a position in the document; determining, from the plurality of visual elements, one or more graphic elements and one or more textual elements; determining a particular graphic element from the one or more graphic elements based on the position of the particular graphic element; determining, from the one or more textual elements, a key that is associated with the particular graphic element; determining, from the one or more textual elements, one or more attributes that are associated with the particular graphic element; generating an association between the key and each of the one or more attributes; and providing a structured representation of the association.

Abstract: Implementations are directed to receiving analytical attack graph (AAG) data representative of one or more AAGs, each AAG representing one or more lateral paths between configuration items within an enterprise network, calculating, for each configuration item in a set of configuration items, a process risk value for each impact in a set of impacts achievable within the configuration item, for a first impact, a first process risk value being calculated based on a multi-path formula in response to determining that multiple paths in the AAG lead to the first impact, and, for a second impact, a second process risk value being calculated based on a single-path formula in response to determining that a single path in the AAG leads to the second impact, and determining that at least one process risk value exceeds a threshold process risk value, and in response, adjusting one or more security controls within the enterprise network.

Abstract: A system to facilitate the exchange and re-use of industrial waste and byproduct materials that have a reuse possibility is disclosed. The system promotes recycling, reusing, and reclaiming of wastes sustainably to ensure compliance and waste minimization. In one embodiment, the system supports an intelligent service for generating recommendations across an industrial stock exchange. Companies looking to dispose of their wastes or byproducts can access the system to initiate and execute trade with other companies looking for materials or compounds that could be reused in their own manufacturing processes. Both parties will be guided toward their shared goal in order to provide a net-zero impact on the environment and maintain an ecological balance.

Abstract: Examples of a system for intelligent communication management for content summarization are provided. In an example, the system receives a summary generation requirement. The system establishes a question database for generation a set of questions for a user corresponding to the summary generation requirement. The system implements an artificial intelligence component to sort the answers obtained from the question and generates a user-specific knowledge database. The system may use the user-specific knowledge model for generation of further questions for the user. The system may assist a user with collecting information in a conversational manner mode and to automatically produce intelligible deliverables. The system may accept multiple modes of input while collecting information from a user. The system may be used for an automatic summary generation for customer service conversations, interviews, conferences and presentations, a person's holiday chronicles, and the like.

Abstract: Examples for the baggage weight prediction system are provided. The system may create a baggage prediction model in response to a carrier baggage weight prediction requirement requesting a total baggage weight to be carried in a future transportation operation by a carrier. The baggage prediction model may be used to determine a predicted baggage to be carried by a plurality of future passengers associated with a future transportation operation. The system may determine a predicted weight for the predicted baggage. The system may sort the predicted baggage into checked baggage and a carry-on baggage. The system may determine a carry-on baggage weight associated with the carry-on baggage based on the baggage prediction model. The system may determine a baggage weight ratio associated with the predicted baggage. The system may generate a baggage prediction result for performing a baggage management action.

Abstract: The present disclosure relates to a system and a method for model control platform stack. The method includes, at an input layer of a model control platform stack, receiving input data. At a governance layer of the model control platform stack, the method includes maintaining a probe and model inventories; selecting a model, a monitoring location point, and a probe; and deploying, based on the selections of the probe and the model, a container to an orchestration layer of the model control platform stack. At the orchestration layer of the model control platform stack, the method includes accessing the container; using the container to deploy the probe and the model; scheduling an execution of the model to determine inference associated with the input data; during the execution, extracting probe data, using the probe, from the monitoring location point; and adjusting, based on the probe data and the inference, the model.

Abstract: The disclosed system and method focus on automatically generating questions from input of written text and/or audio transcripts (e.g., learning materials) to aid in teaching people through testing their knowledge about information they have previously been presented with. These questions may be presented to an end user via a conversational system (e.g., virtual agent or chatbot). The user can iterate through each question, provide feedback for the question, attempt to answer the question, and/or get an answer score for each answer. The disclosed system and method can generate questions tailored to a particular subject by using teaching materials as input. The disclosed system and method can further curate the questions based on various conditions to ensure that the questions are automatically selected and arranged in an order that best suits the subject taught and the learner answering the questions.

Abstract: Implementations of the present disclosure include providing, by a security platform, graph data defining a graph that is representative of an enterprise network, the graph including nodes and edges between nodes, a set of nodes representing respective assets within the enterprise network, and a node representing a process executed within a system of the enterprise, each edge representing at least a portion of one or more lateral paths between assets in the enterprise network, determining, for each asset, a contribution value indicating a contribution of a respective asset to operation of the process, determining, for each asset, an impact value based on a total value of the process and a respective contribution value of the asset, and implementing one or more remediations based on a set of impact values determined for the assets, each remediation mitigating a cyber-security risk within the enterprise network.

Abstract: An automatic artwork review system validates an artwork or a product label based on a received label specification document. Text extracted from the product label is chunked into sentences and words. Character-wise comparison is executed to identify the best match text from the label specification document for the sentence chunks from the product label. If the corresponding best match texts bears a similarity higher than a predetermined threshold to selected text including one or more sentence chunks, no errors are raised. If the similarity of the best match text to the selected text is not higher than the predetermined threshold, the specific errors occurring in the selected text and the particular portions where such errors are present are identified. The information regarding the errors can be output via one or more of an output user interface or a label compliance report.

Abstract: A system and method for automatically detecting misinformation is disclosed. The misinformation detection system is implemented using a cross-stitch based semi-supervised end-to-end neural attention model which is configured to leverage the large amount of unlabeled data that is available. In one embodiment, the model can at least partially generalize and identify emerging misinformation as it learns from an array of relevant external knowledge. Embodiments of the proposed system rely on heterogeneous information such as a social media post's text content, user details, and activity around the post, as well as external knowledge from the web, to identify whether the content includes misinformation. The results of the model are produced via an attention mechanism.

Abstract: Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Abstract: Call preparation engine for customer relationship management (“CRM”) is presented. Example embodiments of the present invention include invoking an intelligence assistant to retrieve lead details, customer information, and insights for use during a call between a tele-agent and a customer; administering tele-agent call preparation notes for use during the call between the tele-agent and the customer; and displaying, through a call preparation cockpit, the lead details, customer information, insights and tele-agent call preparation notes.

Abstract: Implementations of the present disclosure include providing a graph that is representative of an enterprise network and includes nodes and edges, a set of nodes representing assets within the enterprise network, each edge representing a lateral movement path between assets, determining, for each asset, a contribution value indicating a contribution of an asset, determining lateral movements paths between a first asset and a second asset, providing a lateral movement path value representative of a difficulty in traversing a respective lateral movement path, identifying a set of remediations based on remediations defined for one or more vulnerabilities associated with issues identified for assets, each remediation mitigating a cyber-security risk within the enterprise network, and prioritizing the two or more remediations based on contribution values of assets, lateral movement path values of paths, and one of lateral movement complexity values of respective segments of paths and costs of respective remediation

Abstract: Systems and methods for evaluating attributes in supply chain management is disclosed. The system may receive data from a set of data sources corresponding to a supply chain associated with at least a product, pre-process the data based on integration of the data from each of the set of data sources, generate supply chain data based on the integrated data, analyze, via an orchestration engine, the supply chain data to assess an impact of the supply chain data on the supply chain, predict, via the orchestration engine, a state associated with a purchase event of the product in the supply chain, and generate a resolution flow to be executed in the supply chain for managing the predicted state associated with the purchase event of the product.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support creation and execution of automated sequences of user interface (UI) interactions. To facilitate creation and execution of automated sequences of UI interactions, an automation engine is provided and includes a model configured to capture image data when creating the sequence of UI interactions. The model may also be used during replay of the sequence of UI interactions. For example, the model may be used during replay of the sequence of UI interactions to locate UI elements corresponding to the UI interactions, or to perform pre- and/or post-validation of action execution. The automation engine may also provide processes to enable location of dynamic content, such as UI elements that may be presented in different or unexpected locations, and processes to address complex UI elements, such as data grids, tree views, and drawings (e.g., CAD drawings).