A network of motion sensors employs sensitive accelerometers to issue time-domain measurements of building movement from multiple locations within and between buildings and other structures. The time-domain measurements from the various motion sensors are synchronized and converted into frequency-domain measurements of building movement. Individual motion sensors can be equipped with the requisite processor and memory to synchronize and covert the time-domain measurements. The motions sensors can classify detected events into various event types, such as earthquakes, wind events, or bipedal locomotion. The sensors can also communicate with one another or other resources to calculate event probabilities. A motion sensor may, for example, receive an earthquake-verification signal responsive to an earthquake-verification request. The network of motion sensors can calculate local soil stiffness and financial loss estimations responsive to their individual or collective frequency-domain measurements.

Provided are a data processing method and device, and a computer-readable storage medium. The data processing method includes: acquiring at least one type of weights and feature data corresponding to each type of the weights; acquiring, according to the at least one type of weights, a classification feature corresponding to each type of the weights; performing calculation according to the at least one type of weights and the feature data corresponding to each type of the weights to obtain a first processing result corresponding to each type of the weights; and performing calculation according to the classification feature and the first processing result corresponding to each type of the weights corresponding to each type of the weights to obtain a second processing result.

With respect to training data records in which combinations of data item values of data items are individually associated with label information, the data item values are converted based on a criterion per data item into discretized data values. Training processing for training a model that receives the discretized data values as input and performs determination about the label information is performed by using training data records obtained by the conversion. From an execution result of the training processing, feature data records, each of which differently indicates a combination of two or more data items for the determination among the data items, and index values, which indicate importance levels of the feature data records respectively, are acquired. The criterion for the discretization of the data item values is changed based on at least one of the feature data records having been selected based on the index values.

A method for training thresholds controlling data flow in a plurality of cascaded classifiers for classifying malicious software, comprising: in each of a plurality of iterations: computing a set of scores, each for one of a set of threshold sequences, each threshold sequence is a sequence of sets of classifier output thresholds, each set of classifier output thresholds used to control a flow of data from a first cascaded classifier of the plurality of cascaded classifiers to a second cascaded classifier of the plurality of cascaded classifiers, each score computed when classifying, using the respective threshold sequence, each of a plurality of software objects as one of a set of maliciousness classes; computing a set of new threshold sequences by applying a genetic algorithm to the set of threshold sequences and the set of scores; and using the set of new threshold sequences in a consecutive iteration.

An inspection method includes receiving a plurality of training images and an image of a target object obtained from inspection of the target object. The method further includes generating, by one or more training codes, a plurality of inference codes. The one or more training codes are configured to receive the plurality of training images as input and output the plurality of inference codes. The one or more training codes and the plurality of inference codes includes computer executable instructions. The method further includes selecting one or more inference codes from the plurality inference codes based on a user input and/or one or more characteristics of at least a portion of the received plurality of training images. The method also includes inspecting the received image using the one or more inference codes of the plurality of inference codes.

Techniques and solutions are described for determining relationships in data with improved efficiency, including computing resource use. A plurality of attributes are selected for analysis. The attributes can be processed, such as to facilitate relationship determination. Relationships between attribute values are determined. Redundant relationships can be removed. Distances are determined between relationships and used to select a sample of relationships. The sample is labelled by a user and used to train a machine learning classifier. The machine learning classifier labels determined relationships.

A network of motion sensors employs sensitive accelerometers to issue time-domain measurements of building movement from multiple locations within and between buildings and other structures. The time-domain measurements from the various motion sensors are synchronized and converted into frequency-domain measurements of building movement. Individual motion sensors can be equipped with the requisite processor and memory to synchronize and covert the time-domain measurements. The motions sensors can classify detected events into various event types, such as earthquakes, wind events, or bipedal locomotion. The sensors can also communicate with one another or other resources to calculate event probabilities. A motion sensor may, for example, receive an earthquake-verification signal responsive to an earthquake-verification request. The network of motion sensors can calculate local soil stiffness and financial loss estimations responsive to their individual or collective frequency-domain measurements.

A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can he assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented. using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.

A computer-implemented method of providing a recommendation as to a fault determination for a motor vehicle collision is disclosed. The method may include receiving unstructured text describing the circumstances of the collision. The unstructured text is evaluated an associated intent related to the circumstances of the motor vehicle collision is identified. The intent is mapped to an internal node of a decision tree corresponding to a set of fault-determination rules. The computer then successively prompts and receive input responsive to the prompting that corresponds to details of the circumstances of the collision. The computer may identify, based on the received input, a path through the decision tree ending at a leaf node that corresponds to a fault-determination rule governing motor vehicle collisions that matches the circumstances of the motor vehicle collision. The recommendation is then provided based on that rule. Related systems and computer-readable media are also disclosed.

A video retention system comprising a camera operated by a recording entity, and a video retention server adapted to receive, analyze, and manage video captured by the camera. The video retention server generates a one or more rules using a plurality of user-specified retention parameters which describe the recording entity and a desired retention objective. The rules embody video retention requirements applicable to the recording entity under applicable laws, regulations, and industry standards, and the video retention server executes the rules to delete unnecessary video files while retaining the video files which are necessary to comply with the video retention requirements associated with the specified retention objectives.