Abstract: Techniques are provided for evaluating a product, during any point in its lifecycle, based on at least a determined carbon footprint and/or a determined authenticity. Specifically, a carbon footprint at a field level granularity can be determined based on input characteristics of a farm, field, product, etc. Advantageously, the carbon footprint can be determined for different fields that are geographically close to each other. The determined carbon footprint may be utilized to modify one or practices at the field level. As such, the carbon footprint at the field level can be improved. Additionally, the determined carbon footprint and other relevant criteria related may be utilized to generate a customer product score that can be utilized by a consumer to make informed purchasing decisions.

Abstract: A glass container for storing pharmaceutical formulations may include a glass body formed from a Type IA or Type IB glass composition according to ASTM Standard E438-92(2011). The glass body may include a wall portion with an inner surface and an outer surface, a heel portion and a floor portion, wherein the inner surface of the glass container is formed by the inner surface of the glass body. The glass body may include at least a class A2 base resistance or better according to ISO 695, at least a type HGB2 hydrolytic resistance or better according to ISO 719 and Type 1 chemical durability according to USP <660>. The glass container does not comprise a boron-rich layer on the inner surface of the glass body in as formed condition.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A coated glass pharmaceutical package includes a glass body having a Type 1 chemical durability according to USP 660, at least a class A2 base resistance or better according to ISO 695, and at least a type HGB2 hydrolytic resistance or better according to ISO 719, the glass body having an interior surface and an exterior surface and a wall extending therebetween. A lubricous coating having a thickness of less than or equal to 90 nm may be positioned on at least a portion of the exterior surface of the glass body but not on any portion of the interior surface. The portion of the coated glass package with the lubricous coating comprises a coefficient of friction that is at least 20% less than an uncoated glass package and the coefficient of friction does not increase by more than 30% after undergoing a depyrogenation cycle including exposure to a temperature of 250° C. for a time period of 30 minutes.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A system for classifying a data item to communicate to authorized users extracts features from the data item, where the features comprise a responsibility feature and a sensitivity feature. The responsibility feature indicates a job responsibility associated with the data item. The sensitivity feature indicates a sensitivity level of the data item. The system determines, based on the responsibility feature, that the data item belongs to a particular responsibility class. The system determines, based on the sensitivity feature, that the data item belongs to a particular sensitivity class. The system determines whether a user to whom the data item is directed belongs to the particular responsibility class and sensitivity class to which the data item belongs. The system sends the data item to the user, if is it determined that the user belongs to the particular responsibility class and sensitivity class to which the data item belongs.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to input the user activity information into a first machine learning model that is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a second machine learning model that is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A device that is configured to receive user activity information that includes information about user interactions with a network device for a plurality of users. The device is further configured to receive a set of bad actor candidates that identifies one or more users from among the plurality of users. The device is further configured to filter the user activity information based on the set of bad actor candidates. The device is further configured to input the filtered user activity information into a machine learning model. The machine learning model is configured to receive the filtered user activity information and to output system exposure information that identifies network security threats. The device is further configured to identify network security actions based on the network security threats and to execute the network security actions.

Abstract: A device that is configured to receive user activity information from a network device. The user activity information includes information about user interactions with the network device for a plurality of users. The device is further configured to input the user activity information into a machine learning model. The machine learning model is configured to receive user activity information and to output a set of bad actor candidates based on the user activity information. The set of bad actor candidates identifies one or more users from among the plurality of users. The device is further configured to receive the set of bad actor candidates from the machine learning model and to output the set of bad actor candidates.

Abstract: A system for classifying a data item to communicate to authorized users extracts features from the data item, where the features comprise a responsibility feature and a sensitivity feature. The responsibility feature indicates a job responsibility associated with the data item. The sensitivity feature indicates a sensitivity level of the data item. The system determines, based on the responsibility feature, that the data item belongs to a particular responsibility class. The system determines, based on the sensitivity feature, that the data item belongs to a particular sensitivity class. The system determines whether a user to whom the data item is directed belongs to the particular responsibility class and sensitivity class to which the data item belongs. The system sends the data item to the user, if is it determined that the user belongs to the particular responsibility class and sensitivity class to which the data item belongs.

Abstract: A system is configured for associating a CVE with a particular device profile is disclosed. The system receives a request from a user to associate a CVE with a particular device profile. For each device profile from a plurality of device profiles stored in a memory, the system determines feature importance values for features of each device profile. The features of each device profile include at least an operating system and a CPU architecture. The feature importance value of a corresponding feature of a device profile associated with a CVE indicates a probability of the CVE to affect the device profile with respect to that feature. The system identifies a device profile that has features with a total feature importance value above a feature importance threshold value. The system identifies a particular CVE associated with the identified device profile. The system associates the particular CVE with the particular device profile.