Abstract: An object detection device includes a memory that stores data, and processing circuitry coupled to the memory. The processing circuitry is configured to set an object point indicating a position of an object in image data, detect a candidate region that is a candidate for an object region where the object in the image data exists, select the candidate region having the object point as an object region, and output the selected candidate region as the object region where the object exists.

Abstract: A line drawing automatic coloring method according to the present disclosure includes: acquiring line drawing data of a target to be colored; receiving at least one local style designation for applying a selected local style to at least one place of the acquired line drawing data; and performing coloring processing reflecting the local style designation on the line drawing data based on a learned model for coloring in which it is learned in advance using the line drawing data and the local style designation as inputs.

Abstract: An apparatus and a method for coloring line drawing is disclosed for: acquiring line drawing data; performing reduction processing on the line drawing data to be a predetermined reduced size to obtain reduced line drawing data; coloring the reduced line drawing data based on a first learned model which is learned in advance using sample data; and coloring original line drawing data with the colored reduced data and the original line drawing data as inputs based on a second learned model which is learned in advance.

Abstract: A machine learning heterogeneous edge device, method, and system are disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, a group determination module, and a leader election module. The edge device analyzes collected data with a model, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group. The edge device determines group membership and determines a leader edge device. The edge device receives a request for the local model, transmits the local model to the leader edge device, receives a mixed model created by the leader edge device performing a mix operation of the local model and a different local model, and replaces the local model with the mixed model.

Abstract: A machine learning heterogeneous edge device, method, and system are disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, a group determination module, and a leader election module. The edge device analyzes collected data with a model, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group. The edge device determines group membership and determines a leader edge device. The edge device receives a request for the local model, transmits the local model to the leader edge device, receives a mixed model created by the leader edge device performing a mix operation of the local model and a different local model, and replaces the local model with the mixed model.

Abstract: A control apparatus performs analysis by using partial information and determines whether or not communication is abnormal. If the communication is determined to be abnormal, the control apparatus controls a communication route for a communication control device such that the communication is transmitted from a communication apparatus to the control apparatus. Further, the control apparatus determines whether or not the communication transmitted by the control of the communication route is malicious communication. As a result, if the communication is determined to be malicious communication, the control apparatus controls the communication control device to restrict the malicious communication.

Abstract: [Problem] To provide a learning device for performing more efficient machine learning. [Solution] A learning device unit according to one embodiment comprises at least one learning device and a connection device for connecting an intermediate learning device having an internal state shared by another learning device unit to the at least one learning device.

Abstract: A switching system and a method for performing high-speed switching within a telecommunications system. One aspect of the present invention utilizes an internal host and a self learning routing algorithm to reduce the time required to identify an output channel for routing a communication. Thus, the overhead associated with querying an external database can be eliminated from the switching system allowing a switching system to reduce the call hold time, and thereby meet the call flow demands of the telecommunications system. Another aspect of the present invention is the use of a hashing algorithm in resolving routing decisions. The hashing algorithm operates on received DID numbers to identify an output channel for a received call. The hashing algorithm is able to accept any DID number as input. Therefore, this aspect of the present invention allows a switching system to quickly resolve routing decisions without requiring consecutive DID numbers.