Abstract: Systems and methods for dynamic escrow management are disclosed. A method executed by an escrow management computer program may include: receiving an escrow agreement between a first party and a second party to a deal; extracting, using natural language processing, identifications of and authorized signors for the first party and the second party and terms of the escrow agreement; receiving an escrow deposit from the first party or the second party; confirming that an amount of the escrow deposit meets a term of the escrow agreement; depositing the escrow deposit into an escrow account; receiving an escrow instruction from the first party as a completed instruction template at a web interface; retrieving an identification of authorized signor for the first party; routing the escrow instruction to the authorized signor from the first party; receiving approval for the escrow instruction; and causing execution of the escrow instruction.

Abstract: User input handling by a virtual assistant includes receiving a sequence of natural language statements provided by a user to a virtual assistant, applying a cognitive model to the sequence of statements and determining whether the sequence of statements provides a complete problem description, by the user, to which the virtual assistant is to respond in assisting the user address a problem that the user experiences, prompting the user to provide an additional one or more statements to complete the problem description, based on completing the problem description, formulating and providing a response to the user based on a determined intent of the complete problem description, and monitoring input by the user based on providing the response, the input informing feedback for further training of the cognitive model to identify whether statement sequences provide complete or incomplete problem descriptions.

Abstract: The present teaching relates to method, system, medium, and implementations for authenticating a user. A first request is received to set up authentication information with respect to a user, wherein the first request specifies a type of information to be used for future authentication of the user. It is determined whether the type of information related to the user poses risks based on a reverse information search result. The type of information for being used for future authentication of the user is rejected when the type of information is determined to pose risks.

Abstract: Embodiments herein may e.g. relate to a method performed by a network node (12) for handling one or more operations in a communications network comprising a plurality of computing devices (10,11) performing one or more tasks. The network node (12) obtains an indication of a failure of an operation in the communications network; and obtains one or more parameters to resolve the failure. The one or more parameters relate to resources of the plurality of computing devices (10,11) and the communications network (1), wherein the one or more parameters are structured in an hierarchic manner and defined by a task of a capability, a resource used for the task, and a service level for the task. The network node (12) generates a plan by taking an aimed service level into account as well as the obtained one or more parameters; and executes one or more operations using the generated plan.

Abstract: The present disclosure provides antibodies and antigen-binding fragments thereof that specifically bind to the spike protein of SARS-CoV-2 and methods of using such antibodies and antigen-binding fragments thereof for the prevention and treatment of Coronavirus Disease 2019 (COVID-19) in a subject.

Abstract: A method obtains service request information identifying computing device nodes invoked by users. Based on the service request information, sets of computing device nodes are identified, each set of computing device nodes includes computing device nodes invoked simultaneously or sequentially by one of the users. Communities are further identified based on a probability measure that is a measure of a probability of co-occurrence of two sets of computing device nodes. Each community has sets of computing device nodes each having the probability measure over a probability threshold in relation to at least one other set of computing device nodes in the community. Solutions are predicted for provision of services of the sets of computing device nodes of the communities. Each predicted solution for provision of services relates to a community and is determined based on shared knowledge of predicted solutions for provision of services relating to other communities.

Abstract: Methods and systems for searching and retrieving information. In one aspect, there is a method of retrieving information using a knowledge base. The method comprises receiving a search query entered by a user and using a first model to identify a category corresponding to the received search query. The method further comprises based on the received search query, a loss function of the first model, and an objective function of a second model, identifying T topics corresponding to the received search query, and performing a search for the received search query only on a part of the knowledge base that is associated with the identified category and/or the identified topics. The method further comprises retrieving one or more files associated with the identified category and/or the identified topics.

Abstract: A method performed by a local client computing device is provided. The method includes training a local model using data from the local client computing device, resulting in a local model update; sending the local model update to a central server computing device; receiving from the central server computing device a first updated global model; determining that the first updated global model does not meet a local criteria, wherein determining that the first updated global model does not meet a local criteria comprises computing a score based on the first updated global model, wherein the score exceeds a threshold; in response to determining that the first updated global model does not meet a local criteria, sending to the central server computing device context information; and receiving from the central server computing device a second updated global model.

Abstract: A method obtains service request information identifying computing device nodes invoked by users. Based on the service request information, sets of computing device nodes are identified, each set of computing device nodes includes computing device nodes invoked simultaneously or sequentially by one of the users. Communities are further identified based on a probability measure that is a measure of a probability of co-occurrence of two sets of computing device nodes. Each community has sets of computing device nodes each having the probability measure over a probability threshold in relation to at least one other set of computing device nodes in the community. Solutions are predicted for provision of services of the sets of computing device nodes of the communities. Each predicted solution for provision of services relates to a community and is determined based on shared knowledge of predicted solutions for provision of services relating to other communities.

Abstract: A method for detecting and reducing the impact of deficient nodes in a machine learning system is provided. The method includes receiving a local model update from a first local client node; determining a change in accuracy caused by the local model update; determining that the change in accuracy is below a first threshold; and in response to determining that the change in accuracy is below the first threshold, sending a request to the first local client node signaling the first local client node to compress local model updates.

Abstract: In a method performed by a first node, the node determines a plan based on: i) a first goal to be achieved according to capabilities of the first node, and a first set of first actions to be performed by the first node to achieve the first goal, and ii) for each second node of one or more second nodes: a) a respective goal to be achieved according to respective capabilities of each second node, and b) a respective set of first actions to be individually performed by each second node to achieve the respective goal. The plan is to collaboratively achieve each of the respective goals and the first goal, by determining a respective set of second actions to be respectively performed by each second node and the first node. The first node sends a respective indication indicating the determined second actions.

Abstract: A method on a central node or server is provided.

Abstract: The present disclosure relates to a method performed by a cloud node (104) for handling sensor nodes and fog nodes in a communications system (100), wherein the communications system comprises a plurality of sensor nodes (110) located at a plurality of locations, to be handled by the fog nodes (120), the method comprising obtaining (S300) a first number of sensor nodes and their respective locations, out of said plurality of sensor nodes, to monitor the communications system in its entirety, based on measurements from at least some of the plurality of sensor nodes at their respective locations; determining (S310) a second number of said fog nodes and their respective location, based on the first number of sensor nodes and a connectivity capacity of said second number of fog nodes, where the second number of fog nodes is determined to cover said first number of sensor nodes; ranking (S320) said second number of fog nodes according to a conditional probability of failure for the second number of fog nodes, base

Abstract: A method for explaining deep-learning models is provided. The method includes extracting a set of features from a first deep-learning model for a first set of training data; clustering the set of features into N groups, wherein N represents a number of unique labels in the first set of training data; forming a clustering matrix from the N groups; and determining dominant columns in the clustering matrix to form a subset of the set of features.

Abstract: A method is provided including receiving a first and second registration requests transmitted by a first and second smart containers, including first and second information identifying a first and second capabilities; and receiving a first request transmitted by the first smart container as a result of a failure experienced by the first smart container, identifying a temporary capability. The method further includes, as a result of receiving the first request, determining that the second smart container can perform the temporary capability.

Abstract: Embodiments described herein include a Power over Ethernet (PoE) enabled device that uses 2-event classification when allocating power to coupled powered devices (PDs). If the 2-event classification is successful, power sourcing equipment (PSE) on the PoE device allocates a maximum power allotment to the PD. Once powered on, the PD may determine it does not require the maximum power allotment and negotiate a reduction in the power allotted to the PD.

Abstract: Embodiments described herein include a Power over Ethernet (PoE) enabled device that uses 2-event classification when allocating power to coupled powered devices (PDs). If the 2-event classification is successful, power sourcing equipment (PSE) on the PoE device allocates a maximum power allotment to the PD. Once powered on, the PD may determine it does not require the maximum power allotment and negotiate a reduction in the power allotted to the PD.

Abstract: Embodiments described herein include a Power over Ethernet (PoE) enabled device that uses 2-event classification when allocating power to coupled powered devices (PDs). If the 2-event classification is successful, power sourcing equipment (PSE) on the PoE device allocates a maximum power allotment to the PD. Once powered on, the PD may determine it does not require the maximum power allotment and negotiate a reduction in the power allotted to the PD.

Abstract: Embodiments described herein include a Power over Ethernet (PoE) enabled device that uses 2-event classification when allocating power to coupled powered devices (PDs). If the 2-event classification is successful, power sourcing equipment (PSE) on the PoE device allocates a maximum power allotment to the PD. Once powered on, the PD may determine it does not require the maximum power allotment and negotiate a reduction in the power allotted to the PD.