Abstract:
Selecting an instructional video is provided. It is determined that a query is requesting information on how to perform a procedure. A set of instructional videos are accessed corresponding to the information on how to perform the procedure. Information regarding a user of a client device that submitted the query is retrieved from at least one of a set of databases and a set of monitoring devices located on the user via a network. Physiological changes are predicted in the user's current cognitive state based on the information regarding the user retrieved from the set of databases and the set of monitoring devices. An instructional video is selected in the set of instructional videos corresponding to the information on how to perform the procedure based on the user's current cognitive state indicated in the retrieved information regarding the user of the client device.
Abstract:
Data is obtained specifying a measure of a data set to be analyzed by human experts and allotted time for analysis completion. Based on same, a schedule is created for a primary meeting of human experts, whose aim is to analyze the data. The meeting is evaluated to create rate estimates for hypothetical meeting partitions; and, if the primary meeting is not adhering to schedule and/or can be speeded up, the meeting partitions are simulated until a partitioning scheme is determined that can restore the meeting to schedule and/or speed it up. In another aspect, a model of focus of attention of each member of a group of individuals engaged in an activity requiring cooperation is dynamically updated, and a cooperation index is determined based on the model of focus of attention, an interaction graph, and at least one physiological parameter of at least one of the members.
Abstract:
An SDV on-board computer on an SDV receives an SDV recognition signal from a pedestrian signal transceiver worn by a pedestrian. An SDV signal transceiver on the SDV transmits a pedestrian acknowledgement message to the signal transceiver worn by the pedestrian. The SDV on-board computer receives a pedestrian movement signal from a set of pedestrian sensors that monitor movement of the pedestrian. The SDV on-board computer receives an SDV movement signal from a set of SDV sensors on the SDV that track movement of the SDV. The SDV on-board computer, based on the SDV movement signal and the pedestrian movement signal, directs an SDV control processor on the SDV to modify the movement of the SDV in order to provide the pedestrian with time and space required to avoid the pedestrian being struck by the SDV, and notifies the pedestrian that this SDV movement modification will occur.
Abstract:
A computer-implemented method, system, and/or computer program product causes a self-driving vehicle (SDV) to avoid a physical encounter with an animal. An SDV on-board computer on an SDV receives an animal predicted movement signal from an animal signal transceiver worn by an animal, and an SDV movement signal from a set of SDV sensors on the SDV that track movement of the SDV. One or more processors determine a probability of a physical encounter (E) between the SDV and the animal exceeding a predefined confidence value (C) based on the animal predicted movement signal and the SDV movement signal. In response to determining that E>C, the SDV on-board computer instructs an SDV control processor on the SDV to direct SDV vehicular physical control mechanisms on the SDV to adjust the current speed and direction of movement of the SDV.
Abstract:
A method, system, and/or computer program product dynamically configures a delivery system for delivering products to smart mats. A delivery coordination server determines a location of multiple smart mats. Each of the smart mats includes a positioning system and a transmitter that transmits a message describing the real-time geophysical location of the smart mats. The delivery coordination server receives a message describing a location of a first delivery vehicle that is transporting a first package addressed for delivery to a first smart mat. The delivery coordination server determines that the first smart mat has moved to a location that is within a predetermined distance of a second smart mat, to which a second delivery vehicle is scheduled to deliver a second package. The delivery coordination server directs the first delivery vehicle to transfer the first package to the second delivery vehicle for delivery to the first smart mat.
Abstract:
A security method that includes assigning a sensitivity value for a communication with a sensitivity determining module including at least one hardware processor. Following assignment of the sensitivity value to the communication, the communication is formatted for display. When sensitivity value exceeds a security threshold, the communication is parsed into a sequence of fragments. The communication is transmitted as the sequence of fragments when said sensitivity value exceeds the security threshold.
Abstract:
The method includes receiving a first set of data from a first client device. The method further includes determining a first topic from the first set of data. The method further includes generating a first productivity value for the first topic. The method further includes receiving a second set of date data from a second client device. The method further includes determining a second topic from by the second set of data. The method further includes generating a second productivity value for second set of data. The method further includes comparing the first topic to the second topic. The method further includes in response to comparing the first topic and the second topic and determining the difference between the first productivity value and the second productivity value is above a threshold value, triggering an action.
Abstract:
A technique for defragmenting jobs on processor-based computing resources including: (i) determining a first defragmentation condition, which first defragmentation condition will be determined to exist when it is favorable under a first energy consideration to defragment the allocation of jobs as among a set of processor-based computing resources of a supercomputer (for example, a compute-card-based supercomputer); and (ii) on condition that the first defragmentation condition exists, defragmenting the jobs on the set of processor-based computing resources.
Abstract:
Speech traits of an entity imbue an artificial intelligence system with idiomatic traits of persons from a particular category. Electronic units of speech are collected from an electronic stream of speech that is generated by a first entity. Tokens from the electronic stream of speech are identified, where each token identifies a particular electronic unit of speech from the electronic stream of speech, and where identification of the tokens is semantic-free. Nodes in a first speech graph are populated with the tokens to develop a first speech graph having a first shape. The first shape is matched to a second shape of a second speech graph from a second entity in a known category. The first entity is assigned to the known category, and synthetic speech generated by an artificial intelligence system is modified based on the first entity being assigned to the known category.
Abstract:
A system and method and computer program product for paint color recommendation. The system obtains measures of an environment to be painted and trains a learned model to input data received from customers including data representing each customer's initial color paint and pigment selection, and one or more of: a customer perceptual, a customer context, and environment measure (P/C/E data) to generate a sparse matrix. One or more paint vendors may then use the generated sparse matrix to determine a color pigment recommendation from a pigments color space for a customer. From a user selected color/pigment, and using the learned model, the system maps the selection, together with the user's P/C/E data back to the color/pigments space. User feedback representing a degree of satisfaction that the recommended color pigment applied to the user environment has matched the user's initial color paint and color pigment selection is elicited.
Abstract translation:一种用于油漆颜色推荐的系统和方法以及计算机程序产品。 系统获取待涂漆环境的措施,并训练学习模型,输入客户收到的数据,包括表示每个客户的初始颜色颜料和颜料选择的数据,以及以下一个或多个:客户感知,客户环境和环境措施 (P / C / E数据)生成稀疏矩阵。 然后,一个或多个涂料供应商可以使用所生成的稀疏矩阵来确定颜料颜色从客户的色彩颜色空间推荐。 从用户选择的颜色/颜料,并使用学习的模型,系统将选择与用户的P / C / E数据一起映射回颜色/颜色空间。 表示应用于用户环境的推荐的彩色颜料与用户的初始颜色颜料和颜料颜料选择匹配的用户反馈程度被引出。