Implementations are provided herein for systems, methods, and a non-transitory computer product configured to use predictive analysis of quantifiable parameters associated with individual files stored on a distributed file storage system. In some embodiments, parameters are analyzed by machine learning so that scheduled antivirus scanning can be intelligently conducted. We teach creating a sequential order for scheduled antivirus scanning such that the files most likely to be accessed or needed by users in the future will be scanned for viruses before those files less likely to be accessed. Our teachings encompass the use of heuristic data compiled and analyzed on a per-file basis. We enable system administrators to determine which parameters to prioritize and to set thresholds for antivirus scanning such as time limits.

A diagnostic service system includes one or plurality of factory monitoring systems configured to perform monitoring of at least one machine; a service center management device that is connected with the one or plurality of factory monitoring systems via a network; one or plurality of service centers that are connected with the service center management device; and a plurality of service terminals connected with one service center or each of the plurality of service centers via a service control. The plurality of service terminals are used by each responder capable of fault diagnosis of the machine, and when fault of a machine occurs, one of the plurality of service terminals is selected via the service center management device and the one service center or plurality of service centers.

In an example embodiment, a screening question-based online screening mechanism is provided to assess job applicants automatically. More specifically, job-specific questions are automatically generated and asked to applicants to assess the applicants using the answers they provide. Answers to these questions are more recent than facts contained in a user profile and thus are more reliable measures of an appropriateness of an applicant's skills for a particular job.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, a device, a storage medium and a computer program product, relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, and can be applied to intelligent transportation scenarios. A specific implementation of the method comprises: acquiring a traffic video stream; performing vehicle detection tracking on the traffic video stream to determine whether there is an abnormally stopped vehicle, wherein a stop with a time length exceeding a preset time length belongs to an abnormal stop; and performing a traffic anomaly classification on a video frame corresponding to the abnormal stop using a decision tree to obtain a traffic anomaly type, if there is the abnormally stopped vehicle, wherein the decision tree is generated based on features for a traffic anomaly detection.

There is a need for more effective and efficient data evaluation. This need can be addressed by, for example, techniques for data evaluation in accordance with a shared decision tree data object. In one example, a method includes generating, using a plurality of feature extraction threads, shared evidentiary data; generating, based on a selected shared evidentiary data subset of the shared evidentiary data that correspond to one or more selected nodes of the shared decision tree data object, refined evidentiary data; processing the refined evidentiary data in accordance with the shared decision tree data object to generate an evaluation output and an explanation output; and displaying an evaluation output user interface comprising user interface data describing the evaluation output and the explanation output.

The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.

A knowledge-based system under uncertainties and/or incompleteness, referred to as augmented knowledge base (AKB) is provided, including constructing, reasoning, analyzing and applying AKBs by creating objects in the form E.fwdarw.A, where A is a rule in a knowledgebase and E is a set of evidences that supports the rule A. A reasoning scheme under uncertainties and/or incompleteness is provided as augmented reasoning (AR).

A knowledge-based system under uncertainties and/or incompleteness, referred to as augmented knowledge base (AKB) is provided, including constructing, reasoning, analyzing and applying AKBs by creating objects in the form E.fwdarw.A, where A is a rule in a knowledgebase and E is a set of evidences that supports the rule A. A reasoning scheme under uncertainties and/or incompleteness is provided as augmented reasoning (AR).

Systems and methods for assaying a test entity for a property, without measuring the property, are provided. Exemplary test entities include proteins, protein mixtures, and protein fragments. Measurements of first features in a respective subset of an N-dimensional space and of second features in a respective subset of an M-dimensional space, is obtained as training data for each reference in a plurality of reference entities. One or more of the second features is a metric for the target property. A subset of first features, or combinations thereof, is identified using feature selection. A model is trained on the subset of first features using the training data. Measurement values for the subset of first features for the test entity are applied to thereby obtaining a model value that is compared to model values obtained using measured values of the subset of first features from reference entities exhibiting the property.

Systems and methods for monitoring food intake include an air pressure sensor for detecting ear canal deformation, according to some implementations. For example, the air pressure sensor detects a change in air pressure in the ear canal resulting from mandible movement. Other implementations include systems and methods for monitoring food intake that include a temporalis muscle activity sensor for detecting temporalis muscle activity, wherein at least a portion of the temporalis muscle activity sensor is coupled adjacent a temple portion of eyeglasses and disposed between the temple tip and the frame end piece. The temporalis muscle activity sensor may include an accelerometer, for example, for detecting movement of the temple portion due to mandibular movement from chewing.