Abstract: Data storage and calling methods and devices are provided. One of the methods includes: receiving first motion data and business data; establishing an association relationship between the first motion data and the business data and storing the association relationship; receiving second motion data; and determining first motion data that matches the second motion data, and returning, to a sender of the second motion data, business data associated with the matched first motion data.

Abstract: The interactive toy has a body and means for attaching the body to an object including a part of the user's body, clothing or the like. The body includes a rigid hollow plastic shell with a variety of external touch sensors. The shell contains and protects the electronic components, including a micro-controller connected to the sensors, and to a speaker, vibrator and LED light sources. A fabric layer covers the shell. The attaching means takes the form of a tail. The tail includes a pliable wire encased in a fabric layer. One end of the wire is fixed to the shell such that the tail extends from the back of the body and can be configured to engage an object to support the toy. The toy generates various sounds and lights in response to the sensor outputs.

Abstract: Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M<N and P<M; merging (N?M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N?M+P) feature columns; and training the model based on the training data including the (N?M+P) feature columns.

Abstract: The present disclosure provides feature data processing methods and devices. One exemplary feature data processing method comprises: classifying features into an important feature set and an auxiliary feature set according to information attribute values of the features; converting features in the auxiliary feature set to hash features; and combining the hash features with features in the important feature set, and setting the combined features as fingerprint features. Training and prediction of to-be-processed data can be performed based on the fingerprint features. With the embodiments of the present disclosure, training dimensions can be more controllable and the amount training data amount can be reduced. Therefore, the efficiency of data processing can be improved.

Abstract: A computer implemented method for matching data records from multiple entities comprising providing respective transaction logs for the entities representing actions performed by or in respect of the entities, determining a matching score using the transaction logs for respective pairs of the entities and for predetermined combinations of merged entities by generating a measure representing a gain in behavior recognition for the entities before and after merging, and using the gain as a matching score.

Abstract: An apparatus and method for interference-avoidance in a hybrid communications network is provided. The method includes obtaining related information for interfering cellular user equipment in the hybrid network, reporting the related information for the interfering cellular user equipment to an evolved Node B (eNB), receiving a list of component carriers and competition related information for the component carriers, and selecting one or more proper component carriers from the list of component carriers according to certain rules.

Abstract: A computer implemented method for matching data records from multiple entities comprising providing respective transaction logs for the entities representing actions performed by or in respect of the entities, determining a matching score using the transaction logs for respective pairs of the entities and for predetermined combinations of merged entities by generating a measure representing a gain in behavior recognition for the entities before and after merging, and using the gain as a matching score.

Abstract: A Bayesian approach to training in conditional random fields takes a prior distribution over the modeling parameters of interest. These prior distributions may be used to generate an approximate form of a posterior distribution over the parameters, which may be trained with example or training data. Automatic relevance determination (ARD) may be integrated in the training to automatically select relevant features of the training data. From the trained posterior distribution of the parameters, a posterior distribution over the parameters based on the training data and the prior distributions over parameters may be approximated to form a training model. Using the developed training model, a given image may be evaluated by integrating over the posterior distribution over parameters to obtain a marginal probability distribution over the labels given that observational data.