Abstract: Various embodiments of systems and methods for mobile crowd sensing applications are disclosed herein.

Abstract: Methods and systems for utilizing co-simulation of a physical model and a self-adaptive predictive controller using hybrid automata are described. For example, there is disclosed a self-adaptive predictive control system to generate an initial patient model with initial patient model settings (k1, k2, . . . , kn) and execute a program that takes as input, the initial patient model settings as configuration parameters that specify at least a plurality of initial states. Such a system further receives new patient inputs and generates hybrid automata as a variation of the initial states by applying the new patient inputs to the initial patient model. The system further derives reachable states from the hybrid automata and outputs all reachable states computed. The system further detects changes and responsively generates a new patient predictive model with new parameter settings (k?1, k?2, . . . , k?n), iteratively repeating until a termination criterion is satisfied. Other related embodiments are disclosed.

Abstract: A communication pathway between a plurality of network nodes within a network is established. A DIS election operation is executed to determine a first network node among the plurality of network nodes as the DIS for the network and creating a first pseudo node for the first network node, and with each network node of the plurality of network nodes, determining whether the connectivity between the first network node and the other network nodes of the plurality of network nodes within the network is in a synchronous state with the adjacencies with the other network nodes of the plurality of network nodes within the network.

Abstract: Systems and methods are provided for controlling predictive medical monitoring. A non-linear-predictive-guide model estimates a patient parameter that is used as a guide in a deep neural network for improving accuracy of estimation by the deep neural network. The guide model generates a guiding first estimated patient parameter based on the guide model and patient input data. The deep neural network generates a second estimated patient parameter based on the deep neural network, the patient input data, and the guiding first estimated patient parameter. The deep neural network includes an input layer that receives the guiding first estimated patient parameter, and hidden layers including respective artificial neurons configured to perform a linear or nonlinear transformation on output of at least one artificial neuron from an adjacent layer in the deep neural network. An output layer receives at least one output from a hidden layer.

Abstract: Various embodiments of systems and methods for mobile crowd sensing applications are disclosed herein.

Abstract: A communication pathway between a plurality of network nodes within a network is established. A DIS election operation is executed to determine a first network node among the plurality of network nodes as the DIS for the network and creating a first pseudo node for the first network node, and with each network node of the plurality of network nodes, monitoring connectivity between the first network node and the other network nodes of the plurality of network nodes within the network, and monitoring adjacencies with the other network nodes of the plurality of network nodes within the network. Whether the connectivity between the first network node and the other network nodes of the plurality of network nodes within the network is in a synchronous state with the adjacencies with the other network nodes of the plurality of network nodes may be determined.

Abstract: Customized feature vectors are used to train a machine learning algorithm to automatically identify a network component where a network fault has occurred. A database comprising network components and associated network faults is analyzed to select a set of network components associated with the largest quantity of network faults. Customized features associated with the network faults are identified and selected for use in a feature vector as input to a machine learning algorithm. The features are selected based upon analysis of consistency checks, component configuration limits, and network wide configurations.

Abstract: Various embodiments of systems and methods for a learning environment for accessible virtual computer science education are disclosed.

Abstract: Aspects of the disclosed technology address limitations relating to packet replication for multi-destination traffic, by providing methods for performing hardware-based replication in network infrastructure devices, such as switches. In some aspects, application specific integrated circuits (ASICs) resident in physical devices can be used to perform packet replication. Depending on implementation, a hardware-based replication process can include steps for receiving a first packet that includes a first outer header containing first address information, receiving a second packet including a second outer header containing a hardware replication flag, forwarding the first packet to all virtual tunnel endpoints (VTEPs) connected with the TOR switch, and performing hardware replication for the second packet based on the hardware replication flag to generate one or more unicast packets. Systems and machine readable media are also provided.

Abstract: In accordance with one example embodiment, a system configured for programming a network layer multicast address entry in a routing table of an ingress line card module is disclosed. The network layer multicast address entry includes a network layer address associated with at least one egress line card. The system is further configured for programming a data link layer multicast routing address entry in a routing table of a fabric card module in which the data link layer multicast routing address entry corresponds to the network layer multicast address entry.

Abstract: Various embodiments of a system and associated method for language-driven gesture understanding are disclosed herein.

Abstract: Various embodiments of a system and associated method for evaluating handwashing protocol compliance are disclosed herein. The system considers temporal combinations of segments of a test handwashing routine to recognize components of a compliant handwashing routine within the test handwashing routine. The system determines whether the components of the handwashing routine have been adequately performed and provides actionable feedback to a user to improve adherence to handwashing routine guidelines.

Abstract: Examples described herein relate to a network interface device comprising: a host interface, a direct memory access (DMA) engine, and circuitry to allocate a region in a cache to store a context of a connection. In some examples, the circuitry is to allocate a region in a cache to store a context of a connection based on connection reliability and wherein connection reliability comprises use of a reliable transport protocol or non-use of a reliable transport protocol. In some examples, the circuitry is to allocate a region in a cache to store a context of a connection based on expected length of runtime of the connection and the expected length of runtime of the connection is based on a historic average amount of time the context for the connection was stored in the cache. In some examples, the circuitry is to allocate a region in a cache to store a context of a connection based on content transmitted and the content transmitted comprises congestion messaging payload or acknowledgement.

Abstract: In accordance with one example embodiment, a system configured for programming a network layer multicast address entry in a routing table of an ingress line card module is disclosed. The network layer multicast address entry includes a network layer address associated with at least one egress line card. The system is further configured for programming a data link layer multicast routing address entry in a routing table of a fabric card module in which the data link layer multicast routing address entry corresponds to the network layer multicast address entry.

Abstract: Various embodiments disclosed herein relate to systems and methods for an intelligent autonomous decision making module that maximizes the return of the most scientifically relevant dataset over the low bandwidth for experts to analyze further. A rule based knowledge extraction methodology is disclosed, guided by expert knowledge for all scientifically relevant geological landforms with respect to expert selected attributes. The datasets are subsequently prioritized based on how novel the instances are with respect to its rule and is used to update the rules. The effectiveness of the proposed approach is then determined by evaluating how acceptable the prioritization order is to experts and explaining the decisions to increase the interpretability of the assigned priority.

Abstract: Systems and methods for mining hybrid automata from input-output traces of cyber-physical systems are disclosed herein.

Abstract: Techniques for implementing deadlock avoidance in a leaf-spine network are described. In one embodiment, a method includes monitoring traffic of a plurality of packets at a leaf switch in a network having a leaf-spine topology. The method includes marking a packet with an identifier associated with an inbound uplink port of the leaf switch when the packet is received from one of a first spine switch and a second spine switch. The method includes detecting a valley routing condition upon determining that the packet marked with the identifier is being routed to an outbound uplink port of the leaf switch to be transmitted to the first spine switch or the second spine switch. Upon detecting the valley routing condition, the method includes dropping packets associated with a no-drop class of service when a packet buffer of the inbound uplink port reaches a predetermined threshold.

Abstract: This disclosure describes techniques for collecting network parameter data for network switches and/or physical servers and provisioning virtual resources of a service on physical servers based on network resource availability. The network parameter data may include network resource availability data, diagnostic constraint data, traffic flow data, etc. The techniques include determining network switches that have an availability of network resources to support a virtual resource on a connected physical server. A scheduler may deploy virtual machines to particular servers based on the network parameter data in lieu of, or in addition to, the server utilization data of the physical servers (e.g., CPU usage, memory usage, etc.). In this way, a virtual resource may be deployed to a physical server that has an availability of the server resources, but also is connected to a network switch with the availability of network resources to support the virtual resource.

Abstract: Systems and methods are described for facilitating non-verbal communication. An electronic processor receives data from the hand-gesture sensor indicative of one or more gestures performed by a user, determines at least one word or phrase corresponding to the data from the hand-gestures sensor, and outputs a text representation of the at least one word or phrase on the display screen. Some systems output the text representation as a natural language sentence based on the data from the hand-gesture sensor and linguistic prosody information determined based, at least in part, on image data of the user captured while performing the one or more gesture. Some systems also includes at least one brain sensor and generate an alternative natural language text sentence in response to detecting a signal from the brain sensor indicative of a contradiction response after displaying the natural language text sentence on the display screen.

Abstract: Systems, methods, and non-transitory computer-readable storage media for performing hierarchical routing are disclosed. The method includes identifying routes in a computer network and arranging those routes in two separate routing tables. The first routing table is stored on a first module and the second routing table is stored on a second module.