An emotion analysis method and an electronic apparatus thereof are provided. The emotion analysis method is adapted to the electronic apparatus having a database or connected to the database in order to analyze an emotion of an examinee. The emotion analysis method includes: obtaining a heart rate signal of the examinee; defining a plurality of candidate emotions from the database; analyzing the heart rate signal to obtain a plurality of target emotion parameters; and analyzing the target emotion parameters to determine one of the candidate emotions corresponding to the heart rate signal by applying an emotion analysis model.

The present disclosure is directed to a technique to reduce latency in feedback-based system performance determination. A system receives, from an application developer device, indications of an in-application event and a first input value for an application content delivery profile. The system receives, via an interface from an application developed by an application developer and executed by a computing device remote from the data processing system and different from the application developer device, a ping indicative of an occurrence of the in-application event on the computing device. The system merges data from the ping with internal data determined by the data processing system to generate merged data. The system determines a predicted performance for the in-application event and provides an indication of the predicted performance. The system configures, responsive to the indication of the predicted performance, the application content delivery profile with a second input value.

The present application relates to the field of computer technologies, and in particular, to a stop word identification method used in an information retrieval system. In a stop word identification method, after a first query input by a user is acquired, a second query that belongs to a same session as the first query is acquired, and a stop word in the first query is identified according to a change-based feature of each word in the first query relative to the second query. According to the solution provided by the present application, a stop word in a query can be identified more accurately, and efficiency and precision of an information retrieval system are improved.

Technologies for a distributed Internet of Things (IoT) system are disclosed. Several IoT devices may form a peer-to-peer network without requiring a central server. Information may be stored in a distributed manner in the distributed IoT system, allowing for storing information without transmitting it to a remote server, which may be costly and introduce security or privacy risks. Each IoT device of the distributed IoT system includes a machine learning algorithm that is capable of uncovering patterns in the input of the distributed IoT system, such as a pattern of user inputs in certain situations, and the distributed IoT system may adaptively anticipate a user's intentions.

A decision tree generating apparatus includes an information gain calculator and a decision tree generator. When a classification target data set including a plurality of pieces of classification target data respectively having different attributes with attribute values assigned thereto is segmented into subsets in a form of a decision tree, the information gain calculator calculates an amount of entropy reduction on each attribute, and calculates an information gain, based on the amount of reduction in the entropy and reliability of a user's answer responsive to an inquiry asking about the attribute. The decision tree generator successively determines an attribute having a maximum information gain to be a node of the decision tree by recursively iterating the segmentation of the pre-segmentation data set, and generates the decision tree that is to be used to determine an order of the inquiries.

Provided are a mapping rule updating method, device and system, for timely updating a mapping rule so as to avoid generating a wrong evaluation result. A mapping rule updating method, includes receiving first data generated in a production process; determining a first execution condition of the production process according to the first data based on a mapping rule; generating an evaluation result, the evaluation result being used for indicating whether the first execution condition is in line with a production plan of the production process; acquiring feedback information, the feedback information including indication information for indicating whether the evaluation result is correct; and updating the mapping rule according to the feedback information. As the mapping rule can be automatically updated, the work load of manually configuring the mapping rule is reduced greatly, and the accuracy of the mapping rule and the timeliness of the update is improved.

The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., tessellate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

A social networking system infers a sentiment polarity of a user toward content of a page. The sentiment polarity of the user is inferred based on received information about an interaction between the user and the page (e.g., like, report, etc.), and may be based on analysis of a topic extracted from text on the page. The system infers a positive or negative sentiment polarity of the user toward the content of the page, and that sentiment polarity then may be associated with any second or subsequent interaction from the user related to the page content. The system may identify a set of trusted users with strong sentiment polarities toward the content of a page or topic, and may use the trusted user data as training data for a machine learning model, which can be used to more accurately infer sentiment polarity of users as new data is received.

A mixed semiconductor-superconductor platform is fabricated in phases. In a masking phase, a dielectric mask is formed on a substrate, such that the dielectric mask leaves one or more regions of the substrate exposed. In a selective area growth phase, a semiconductor material is selectively grown on the substrate in the one or more exposed regions. In a superconductor growth phase, a layer of superconducting material is formed, at least part of which is in direct contact with the selectively grown semiconductor material. The mixed semiconductor-superconductor platform comprises the selectively grown semiconductor material and the superconducting material in direct contact with the selectively grown semiconductor material.

A security monitoring system for a Controller Area Network (CAN) comprises an Electronic Control Unit (ECU) operatively connected to the CAN bus. The ECU is programmed to classify a message read from the CAN bus as either normal or anomalous using an SVM-based classifier with a Radial Basis Function (RBF) kernel. The classifying includes computing a hyperplane curvature parameter γ of the RBF kernel as γ=f(D) where f( ) denotes a function and D denotes CAN bus message density as a function of time. In some such embodiments γ=f(Var(D)) where Var(D) denotes the variance of the CAN bus message density as a function of time. The security monitoring system may be installed in a vehicle (e.g. automobile, truck, watercraft, aircraft) including a vehicle CAN bus, with the ECU operatively connected to the vehicle CAN bus to read messages communicated on the CAN bus. By not relying on any proprietary knowledge of arbitration IDs from manufacturers through their dbc files, this anomaly detector truly functions as a zero knowledge detector.