Abstract: Provided is a multi-functional integrated molecular test cartridge and application thereof. The multi-functional integrated molecular test cartridge includes a deformable assembly, a blocking assembly and a reaction assembly, where the blocking assembly is placed between the deformable assembly and the reaction assembly, and a channel in communication with the reaction assembly and the deformable assembly is formed on the blocking assembly. The multi-functional integrated molecular test cartridge can be used for isothermal amplification reactions such as cross priming amplification (CPA), recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP) and rolling circle amplification (RCA), can achieve integrated pre-loading of all reagents, a whole-process integrated test from a sample to a result, and flexible sample processing modes, and has high biological safety and a low risk of product and sample leakage.

Abstract: Embodiments of the present disclosure relate to speech tendency classification.

Abstract: Embodiments of the present disclosure relate to feature crossing for machine learning. According to example embodiments of the present disclosure, a method comprises determining a semantic correlation relationship between a plurality of feature categories, the semantic correlation relationship indicating respective degrees of semantic correlation between respective pairs of feature categories among the plurality of feature categories; obtaining at least two features classified in at least two of the plurality of feature categories for machine learning; and performing feature crossing on the at least two features based on the semantic correlation relationship.

Abstract: Implementations of the present disclosure relate to methods, devices, and computer program products for event processing. In the method, first data associated with a first time window is obtained, the first data comprising a first object and a first group of events that are related to the first object. Second data associated with a second time window is obtained, the second data comprising a second object and a second group of events that are related to the second object, the second time window being different from the first time window. An event model describing an association relationship between an object and an event that is related to the object is determined based on the first and second data. With these implementations, multiple time windows are used in determining the event model, and thus the event model may have better performance in accuracy and immediacy aspects.

Abstract: A method for measuring a dimension of a plant can calculate the dimension of a dimensional measurement point of a plant stem efficiently with high accuracy using an improved method for analyzing a captured image of a plant stem. The method includes, in capturing an image of a predetermined dimensional measurement point of a plant stem with an imager, placing, behind the plant, a background plate including a dimensional reference, and calculating the dimension of the plant stem at the dimensional measurement point based on data obtained at multiple points along the plant stem in image analysis.

Abstract: Embodiments of the present disclosure relate to semantic parsing for short text. According to embodiments of the present disclosure, a method is proposed. The method comprises: obtaining a set of sentences associated with a short text, each of the set of sentences containing all of words in the short text; determining a set of syntactic features associated with the set of sentences, each of the set of syntactic features indicating at least one of a constituency relation and a dependency relation of the corresponding sentence; and determining a semantic structure of the short text based on the set of syntactic features.

Abstract: Embodiments of the present disclosure relate to attribute and rating co-extraction. According to embodiments of the present disclosure, a method is proposed. The method comprises: determining, by a first sub-network of a model, a first feature representation based on a first token contained in a text, the first feature representation indicating semantic information of the first token in the text; determining, by a second sub-network of the model, first attribute information associated with the first token based on the first feature representation, the first attribute information indicating a first attribute involved in the text; and determining, by a third sub-network of the model, first rating information associated with the first token based on the first feature representation, the first rating information indicating a rating related to the first attribute.

Abstract: Implementations of the present disclosure relate to methods, devices, and computer program products of extracting a feature for multimedia data that comprises a plurality of medium types. In a method, a first feature is determined for a first medium type in the plurality of medium types by masking a portion in a first medium object with the first medium type. A second feature is determined for a second medium type other than the first medium type in the plurality of medium types. The feature is generated for the multimedia data based on the first and second features. With these implementations, multiple medium types are considered in the feature extraction, and thus the feature may fully reflect various aspects of the multimedia data in an accurate way.

Abstract: A method for measuring a dimension of a plant can measure the dimension of a plant efficiently with high accuracy. A method for measuring a dimension of a plant with an image of the plant captured with an imager includes placing, behind the plant, a background plate including a dimensional reference in capturing the image of the plant with the imager, capturing an image of a dimensional measurement point of the plant multiple times with a distance between the dimensional measurement point and the background plate being unchanged and with an imaging distance being changed by moving the imager, and measuring the dimension of the plant based on a value of a dimension at the dimensional measurement point in multiple images resulting from capturing the image multiple times using the dimensional reference in the background plate and on a range of imaging with the imager in each of the images.

Abstract: To measure a dimension of a plant efficiently with high accuracy, a tool for measuring a dimension of a plant is placed behind the plant when an image of the plant is captured. The tool for measuring a dimension of a plant includes a background portion including a mark as a positional reference for a growth point of the plant in capturing an image of a predetermined dimensional measurement point of a stem of the plant with an imager, and a dimensional reference portion including a dimensional reference in the captured image. The background portion includes a vertical portion with a horizontal width of 3 to 20 cm inclusive to extend vertically when placed behind the plant.

Abstract: There are provided methods, devices, and computer program products for similarity determination. In a method, first and second access data are obtained for a first and a second group of users who access a first and a second website, respectively. A first and a second jump path are generated for the first and second groups of users based on the first and second access data, respectively. The first and second jump paths describe access history for the first and second groups of users among webpages in the first and second websites, respectively. A similarity is determined between the first and second websites based on the first and second jump paths. Here, access data are used for similarity determination and unvisited webpages are not considered in the similarity determination. Therefore, the computation workload may be lowered, and the noise caused by the unvisited webpages may be reduced.

Abstract: An information processing apparatus calculates an accurate index for the growth state of a plant without increasing a user load. The apparatus includes a receiver that receives an input of a captured image obtained by capturing an image of a plant cultivation area including a rectangular area in a longitudinal direction of the rectangular area, a conversion factor calculator that calculates conversion factors at positions in a depth direction in the captured image by determining a pixel count in a width direction of the rectangular area in the captured image input into the receiver at each position in the depth direction and dividing an actual width of the rectangular area by the determined pixel count, and an index calculator that calculates an index for a growth state of the plant by analyzing the captured image using the conversion factors at the positions calculated by the conversion factor calculator.

Abstract: An information processing apparatus calculates an index for a plant growth state with a neural network using fewer images to achieve training of the neural network. The apparatus includes first to N-th image analyzers (N?2) each analyzing a cultivation area image of a plant cultivation area with a neural network to calculate a state index indicating a growth state of the plant in the cultivation area and including the neural network trained using cultivation area images each having a predetermined growth index classified into a corresponding class of first to N-th growth index classes, and a selector receiving an input of a cultivation area image for which the state index is calculated and causing one of the first to N-th image analyzers trained using cultivation area images classified into the same growth index class as the input cultivation area image to analyze the input cultivation area image.

Abstract: A method includes obtaining, using at least one processor of an electronic device, a base model trained to perform natural language understanding. The method also includes generating, using the at least one processor, a first model expansion based on knowledge from the base model. The method further includes training, using the at least one processor, the first model expansion based on first utterances without modifying parameters of the base model. The method also includes receiving, using the at least one processor, an additional utterance from a user. In addition, the method includes determining, using the at least one processor, a meaning of the additional utterance using the base model and the first model expansion.

Abstract: An electronic device for training a machine learning model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to train a classification layer of the model. To train the classification layer, the at least one processor is configured to receive, by the classification layer, one or more language contexts from an utterance encoder layer and to classify, by the classification layer, at least one portion of an utterance into an information type among a plurality of information types. The at least one processor may be further configured to jointly train a slot filling layer and an intent detection layer of the model.

Abstract: A method includes obtaining, using at least one processor of an electronic device, a base model trained to perform natural language understanding. The method also includes generating, using the at least one processor, a first model expansion based on knowledge from the base model. The method further includes training, using the at least one processor, the first model expansion based on first utterances without modifying parameters of the base model. The method also includes receiving, using the at least one processor, an additional utterance from a user. In addition, the method includes determining, using the at least one processor, a meaning of the additional utterance using the base model and the first model expansion.

Abstract: An electronic device for training a machine learning model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to train a classification layer of the model. To train the classification layer, the at least one processor is configured to receive, by the classification layer, one or more language contexts from an utterance encoder layer and to classify, by the classification layer, at least one portion of an utterance into an information type among a plurality of information types. The at least one processor may be further configured to jointly train a slot filling layer and an intent detection layer of the model.