Abstract: A temperature sensor (116) comprises an electrical circuit (204) including a thermistor (206) intended to be placed next to an object (112); a temperature measurement module (222) configured for determining a measured temperature (T_M) of the thermistor (206) from a resistance of the thermistor (206); and a command module (224) configured for providing a command (C) to the electrical circuit (204) for modifying the electrical circuit (204) in a way that changes a current (IT) flowing in the thermistor (206). The temperature sensor (116) further comprises a thermal loss determination module (226) configured for determining a thermal loss (L) of the thermistor (206) from the thermistor measured temperature (T_M) and the command (C); and a temperature estimation module (228) configured for estimating a temperature (T0_E) of the object (112) from the thermistor measured temperature (T_M) and from the thermal loss (L) of the thermistor (TH).

Abstract: Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.

Abstract: A temperature sensor (116) comprises an electrical circuit (204) including a thermistor (206) intended to be placed next to an object (112); a temperature measurement module (222) configured for determining a measured temperature (T_M) of the thermistor (206) from a resistance of the thermistor (206); and a command module (224) configured for providing a command (C) to the electrical circuit (204) for modifying the electrical circuit (204) in a way that changes a current (IT) flowing in the thermistor (206). The temperature sensor (116) further comprises a thermal loss determination module (226) configured for determining a thermal loss (L) of the thermistor (206) from the thermistor measured temperature (T_M) and the command (C); and a temperature estimation module (228) configured for estimating a temperature (T0_E) of the object (112) from the thermistor measured temperature (T_M) and from the thermal loss (L) of the thermistor (TH).

Abstract: Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.

Abstract: Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.

Abstract: Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.

Abstract: Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.

Abstract: Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting a language of a text string. Consistent with some embodiments, the method may include applying multiple language identification models to a text string. Each language identification model provides a predicted language of the text string and a confidence score associated with the predicted language. The method may further include weighting each associated confidence score based on historical performance of the corresponding language identification model in predicting languages of other text strings. The method may further include selecting a predicted language of the text string from among the multiple predicted languages provided by the multiple language identification models based on a result of the weighting of the confidence score associated with the particular predicted language.