Abstract: The present disclosure provides a deep learning assignment processing method and apparatus, a device and a storage medium. It is feasible to obtain the deep learning assignment submitted by the user in a predetermined manner, the predetermined manner comprising the web UI manner, then submit the deep learning assignment to the deep learning system so that the deep learning system runs the submitted deep learning assignment. As compared with the prior art, processing such as programming is not needed upon submitting the deep learning assignment in the solutions of the present disclosure, thereby simplifying the user's operations, improving the processing efficiency of the deep learning assignment, and accelerating the user's speed of developing deep learning.

Abstract: The present disclosure provides a method and apparatus for scheduling a cloud server. A specific implementation mode of the method comprises: monitoring whether current time is in a first pre-set time period; in response to the monitoring that the current time is in the first pre-set time period, scheduling a cloud server in a first cloud server cluster having a running state being an idle state, as a target cloud server, to a second cloud server cluster, so that the target cloud server executes a task obtained by the second cloud server cluster; monitoring whether the current time is in a second pre-set time period; in response to the monitoring that the current time is in the second pre-set time period, rescheduling the target cloud server to the first cloud server cluster, so that the target cloud server executes a task obtained by the first cloud server cluster.

Abstract: A system comprises: a data warehouse, a storage device and a cluster including a plurality of computing nodes; the data warehouse is configured to store task data obtained from the user; at least one computing node in the cluster includes a resource scheduling component, and is configured to perform resource scheduling for the task and determine a computing node executing the task; the computing node executing the task comprises a model training component and/or a prediction component; the model training component is configured to, according to task data, invoke a corresponding type of learning model from the storage device; use sample data and training target included in the task data to train the learning model, to obtain the prediction model corresponding to the task and store the prediction model in the storage device; the prediction component is configured to obtain a prediction result output by the prediction model.

Abstract: An integrated facial recognition method and system. The method includes: receiving a request for acquiring an integrated facial recognition service sent by a user terminal, which includes: an identifier of a user-selected model associated with facial recognition of the user, and an identifier of an operation selected by the user from candidate operations; and executing distributedly an operation selected by the user from the candidate operations on the user-selected model associated with the facial recognition of the user to obtain an operation result, and storing the operation result. The embodiment has realized completing the operations such as training a model or developing a facial recognition application, without the need of buying hardware and establishing a software environment by the user, thereby saving the development cost and improving the convenience of using the facial recognition service.

Abstract: The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.

Abstract: The present disclosure discloses a method, apparatus and system for updating a deep learning model. A specific embodiment of the method includes: receiving a new training data set sent by a client, the new training data set being detected by the client in a preset path; training a preset deep learning model based on the new training data set to obtain a trained prediction model; and updating the preset deep learning model to the prediction model so that the prediction model is used to perform a data prediction operation online. This embodiment realizes the docking with the training data set of the user and improves the update efficiency of the deep learning model.

Abstract: The present disclosure provides a method and apparatus for scheduling a cloud server. A specific implementation mode of the method comprises: monitoring whether current time is in a first pre-set time period; in response to the monitoring that the current time is in the first pre-set time period, scheduling a cloud server in a first cloud server cluster having a running state being an idle state, as a target cloud server, to a second cloud sever cluster, so that the target cloud server executes a task obtained by the second cloud server cluster; monitoring whether the current time is in a second pre-set time period; in response to the monitoring that the current time is in the second pre-set time period, rescheduling the target cloud server to the first cloud sever cluster, so that the target cloud server executes a task obtained by the first cloud server cluster.

Abstract: The present disclosure provides a method, apparatus and system for detecting structural variations.

Abstract: This disclosure describes a user interface (UI) conceptual model and a data model for quickly and easily developing and managing a GUI. The present methods and systems described herein provide for defining one or more UI elements in the UI conceptual model. The one or more UI elements may further reference one or more model objects in the data model. The one or more model objects may include one or more tasks, which may each be mapped to one or more commands for performing each task, or one or more data items, which may each be mapped to rules or conditions for transforming data. According to embodiments, graphics or functionality of a GUI may be easily and efficiently modified by adding, deleting, or modifying UI elements in the UI conceptual model or by adding, deleting, or modifying referenced model objects in the data model, thereby reducing management and development costs.

Abstract: This disclosure describes a user interface (UI) conceptual model and a data model for quickly and easily developing and managing a GUI. The present methods and systems described herein provide for defining one or more UI elements in the UI conceptual model. The one or more UI elements may further reference one or more model objects in the data model. The one or more model objects may include one or more tasks, which may each be mapped to one or more commands for performing each task, or one or more data items, which may each be mapped to rules or conditions for transforming data. According to embodiments, graphics or functionality of a GUI may be easily and efficiently modified by adding, deleting, or modifying UI elements in the UI conceptual model or by adding, deleting, or modifying referenced model objects in the data model, thereby reducing management and development costs.

Abstract: A method and system for providing an indoor map representation are provided herein. Aspects of this disclosure provide a representation of an indoor map environment whereby navigable areas are represented by a series of convex polygons in adjacency relationships with one another. Image analysis may be performed on a floor plan image to identify navigable areas and to determine the boundaries of the polygons, such as by assigning one or more colors to navigable space and one or more colors to non-navigable space. Route-finding operations may be performed using the adjacency relationships to find a path from a source area to a destination area.

Abstract: A system for automatically generating entity collections comprises a data graph including entities connected by edges and instructions that cause the computer system to determine a set of entities from the data graph and to determine a set of constraints that has a quantity of constraints. A constraint in the set represents a path in the data graph shared by at least two of the entities in the set of entities. The instructions also cause the computer system to generate candidate collection definitions from combinations of the constraints, where each candidate collection definition identifies at least one constraint and no more than the quantity of constraints. The instructions also cause the computer system to determine an information gain for at least some of the candidate collection definitions, and store at least one candidate collection definition that has an information gain that meets a threshold as a candidate collection.

Abstract: Aspects of the present disclosure provide techniques for constructing a scalable model of an indoor space using crowd-sourced inertial navigation system (INS) signals from mobile devices. By tracking INS signals from a number of participating users, the user's trajectories can be estimated as they move their mobile devices indoors. The estimated trajectories can be scored against similar routes taken by other users. Routes with the highest scores are then laid out over a map of the indoor space to identify areas most often traveled to and from landmarks and distances between the landmarks.

Abstract: A method and system for providing an indoor map representation are provided herein. Aspects of this disclosure provide a representation of an indoor map environment whereby navigable areas are represented by a series of convex polygons in adjacency relationships with one another. Image analysis may be performed on a floor plan image to identify navigable areas and to determine the boundaries of the polygons, such as by assigning one or more colors to navigable space and one or more colors to non-navigable space. Route-finding operations may be performed using the adjacency relationships to find a path from a source area to a destination area.

Abstract: This disclosure describes a user interface (UI) conceptual model and a data model for quickly and easily developing and managing a GUI. The present methods and systems described herein provide for defining one or more UI elements in the UI conceptual model. The one or more UI elements may further reference one or more model objects in the data model. The one or more model objects may include one or more tasks, which may each be mapped to one or more commands for performing each task, or one or more data items, which may each be mapped to rules or conditions for transforming data. According to embodiments, graphics or functionality of a GUI may be easily and efficiently modified by adding, deleting, or modifying UI elements in the UI conceptual model or by adding, deleting, or modifying referenced model objects in the data model, thereby reducing management and development costs.

Abstract: A method and system for providing an indoor map representation are provided herein. Aspects of this disclosure provide a representation of an indoor map environment whereby navigable areas are represented by a series of convex polygons in adjacency relationships with one another. Image analysis may be performed on a floor plan image to identify navigable areas and to determine the boundaries of the polygons, such as by assigning one or more colors to navigable space and one or more colors to non-navigable space. Route-finding operations may be performed using the adjacency relationships to find a path from a source area to a destination area.

Abstract: This disclosure describes a user interface (UI) conceptual model and a data model for quickly and easily developing and managing a GUI. The present methods and systems described herein provide for defining one or more UI elements in the UI conceptual model. The one or more UI elements may further reference one or more model objects in the data model. The one or more model objects may include one or more tasks, which may each be mapped to one or more commands for performing each task, or one or more data items, which may each be mapped to rules or conditions for transforming data. According to embodiments, graphics or functionality of a GUI may be easily and efficiently modified by adding, deleting, or modifying UI elements in the UI conceptual model or by adding, deleting, or modifying referenced model objects in the data model, thereby reducing management and development costs.

Abstract: Aspects of the present disclosure provide techniques for constructing a scalable model of an indoor space using crowd-sourced inertial navigation system (INS) signals from mobile devices. By tracking INS signals from a number of participating users, the user's trajectories can be estimated as they move their mobile devices indoors. The estimated trajectories can be scored against similar routes taken by other users. Routes with the highest scores are then laid out over a map of the indoor space to identify areas most often traveled to and from landmarks and distances between the landmarks.

Abstract: A method and system for providing an indoor map representation are provided herein. Aspects of this disclosure provide a representation of an indoor map environment whereby navigable areas are represented by a series of convex polygons in adjacency relationships with one another. Image analysis may be performed on a floor plan image to identify navigable areas and to determine the boundaries of the polygons, such as by assigning one or more colors to navigable space and one or more colors to non-navigable space. Route-finding operations may be performed using the adjacency relationships to find a path from a source area to a destination area.

Abstract: A system for automatically generating entity collections comprises a data graph including entities connected by edges and instructions that cause the computer system to determine a set of entities from the data graph and to determine a set of constraints that has a quantity of constraints. A constraint in the set represents a path in the data graph shared by at least two of the entities in the set of entities. The instructions also cause the computer system to generate candidate collection definitions from combinations of the constraints, where each candidate collection definition identifies at least one constraint and no more than the quantity of constraints. The instructions also cause the computer system to determine an information gain for at least some of the candidate collection definitions, and store at least one candidate collection definition that has an information gain that meets a threshold as a candidate collection.