Abstract: The present invention discloses a contactless and wireless device for measuring solution concentration, including: a measuring machine including a vertical polarization antenna, a horizontal polarization antenna and a contactless measurement means for containing solution to be measured, the contactless measurement means including a liquid container and a microstrip defected ground structure (DGS), the liquid container being disposed on the microstrip DGS, the microstrip DGS including a microstrip line and a double split ring resonator; and a transceiver unit including a transceiver and a dual-polarization receive-transmit antenna connected in communication. In the contactless and wireless device for measuring solution concentration according to the present invention, a double split ring resonator structure is used to detect variation of the solution concentration with high accuracy.

Abstract: Techniques are provided for using machine learning techniques to identify predictive features and predictive values for each feature. In one technique, a model is trained based on training data that comprises training instances, each of which corresponds to multiple usage-based features of an online service by a user. For each usage-based feature in a subset of the usage-based features, the model is used to generate a dependency graph, a histogram is generated, and an optimized value is selected based on the dependency graph and the histogram. A user of the online service is identified, along with a usage value that indicates a level of usage, by the user, of a usage-based feature. A comparison between the usage value and an optimized value of the usage-based feature is performed. Based on the comparison, it is determined whether to present data about that usage-based feature to the user.

Abstract: Techniques are provided for using machine learning techniques to identify predictive features and predictive values for each feature. In one technique, a model is trained based on training data that comprises training instances, each of which corresponds to multiple usage-based features of an online service by a user. For each usage-based feature in a subset of the usage-based features, the model is used to generate a dependency graph, a histogram is generated, and an optimized value is selected based on the dependency graph and the histogram. A user of the online service is identified, along with a usage value that indicates a level of usage, by the user, of a usage-based feature. A comparison between the usage value and an optimized value of the usage-based feature is performed. Based on the comparison, it is determined whether to present data about that usage-based feature to the user.

Abstract: The disclosed embodiments provide a system for inferring successful hires in an online system. During operation, the system samples feedback from users of an online system to generate labels representing hiring outcomes from requests for proposal (RFPs) submitted in the online system. Next, the system inputs the labels with features representing interaction associated with the RFPs as training data for a machine learning model. The system then applies one or more rules derived from the machine learning model to additional features for an additional RFP to infer a hiring outcome for the additional RFP. Finally, the system stores the inferred hiring outcome in association with the additional RFP.