Abstract: A display panel and a display apparatus are described. The display panel includes a plurality of touch units. The touch units include first electrodes and second electrodes that are disposed in a same layer. Adjacent first electrodes along a first direction are connected through a bridge line, and adjacent second electrodes along a second direction are connected through a connecting line. The bridge line is disposed in a different layer from the first electrode, and the connecting line is disposed in a same layer as the second electrode. A display region has a through-hole, and the touch units include a first touch unit that at least partially surrounds the through-hole. The first touch unit includes at least two bridge units, the bridge unit is connected to two adjacent first electrodes along the first direction, and the bridge unit includes at least one bridge line.

Abstract: A back drilling processing method for a circuit board includes: obtaining a board to be back drilled, wherein the board includes a target signal layer and at least two conductive reference layers; drilling a through hole at a set position of the board, and obtaining an actually measured spacing between two conductive reference layers; in response to a hole to be back drilled being of a first type, determining a target back drilling depth; controlling a drilling bit to drill for the target back drilling depth towards the target signal layer; in response to the hole to be back drilled being of a second type, determining a target relative height of a back drilling end point relative to a machine; and controlling the drilling bit to drill for the target relative height towards the target signal layer. The back drilling does not drill through the target signal layer.

Abstract: Embodiments of the present disclosure relate to a method and an apparatus for speech interaction, and a computer readable storage medium. The method may include determining text information corresponding to a received speech signal. The method also includes obtaining label information of the text information by labeling elements in the text information. In addition, the method further includes determining first intention information of the text information based on the label information. The method further includes determining a semantic of the text information based on the first intention information and the label information.

Abstract: The present disclosure provides a method for a human-computer interaction, an apparatus for a human-computer interaction, a device, and a storage medium, and the present disclosure relates to the field of artificial intelligence, such as deep learning and voice. A specific implementation includes: acquiring a voice command; performing voice recognition on the voice command to determine a corresponding voice text; sending, in response to satisfying a preset information sending condition, the voice text to a cloud; receiving a resource for the voice command returned from the cloud; and responding to the voice command based on the resource.

Abstract: The disclosure provides a method and an apparatus for recognizing a speech, an electronic device and a storage medium. Based on obtaining target speech information, state information of an application corresponding to the target speech information and contextual information are obtained. Semantic completeness of the target speech information is obtained based on the state information and the contextual information. A monitoring duration corresponding to the semantic completeness is obtained, and it is monitored whether there is speech information within the monitoring duration. Speech recognition is performed on the target speech information based on no speech information being monitored within the monitoring duration.

Abstract: The disclosure discloses a method for semantic recognition, an electronic device, and a storage medium. The detailed solution includes: obtaining a speech recognition result of a speech to be processed, in which the speech recognition result includes a newly added recognition result fragment and a historical recognition result fragment; obtaining a semantic vector of each historical object in the historical recognition result fragment, and obtaining a semantic vector of each newly added object by inputting the semantic vector of each historical object and each newly added object in the newly added recognition result fragment into a streaming semantic coding layer; and obtaining a semantic recognition result of the speech by inputting the semantic vector of each historical object and the semantic vector of each newly added object into a streaming semantic vector fusion layer and a semantic understanding multi-task layer sequentially arranged.

Abstract: Embodiments of the present disclosure relate to a method and an apparatus for speech interaction, and a computer readable storage medium. The method may include determining text information corresponding to a received speech signal. The method also includes obtaining label information of the text information by labeling elements in the text information. In addition, the method further includes determining first intention information of the text information based on the label information. The method further includes determining a semantic of the text information based on the first intention information and the label information.

Abstract: Provided are a method and an apparatus for acquiring a semantic fragment of a query based on artificial intelligence, and a terminal. The method includes pre-processing the query and determining a first main word and a semantic fragment set included in the query; determining an association degree between each semantic fragment in the semantic fragment set and the first main word according to historical retrieve data; filtering the semantic fragment set according to the association degree and determining an object semantic fragment set corresponding to the query.

Abstract: Provided are a method and an apparatus for acquiring a semantic fragment of a query based on artificial intelligence, and a terminal. The method includes pre-processing the query and determining a first main word and a semantic fragment set included in the query; determining an association degree between each semantic fragment in the semantic fragment set and the first main word according to historical retrieve data; filtering the semantic fragment set according to the association degree and determining an object semantic fragment set corresponding to the query.