Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Techniques for network security are disclosed. In some implementations, an evaluation module determines whether a network communication from a computing device is allowable. The allowability of the communication is determined based on (1) whether the computing device is using an authorized source network address, and (2) whether a non-modifiable identifier of the computing device is authorized. The non-modifiable identifier is a fixed hardware identifier of the computing device, such as an identifier of a CPU, network interface card, storage device, or the like.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: A distributed network system and method includes a processing unit configured to manage safety data for a plurality of medical devices, a database software component in communication with the processing unit, and a monitoring software component in communication with the processing unit. The monitoring software component is configured to monitor a number of messages between a number of medical devices and the processing unit, to process performance parameters to generate an overall performance index, and to generate an output that is viewable by a user. The output includes relative contributions of each of the performance parameters to the overall performance index, where the overall performance index is generated using a weighting factor associated with each of the performance parameters. The performance parameters include the number of messages waiting to be processed, which has the largest weighting factor, and a disk queue length, which has the smallest weighting factor.

Abstract: Techniques for network traffic filtering and flow control are disclosed. Some implementations provide a network communication evaluation module (“NCEM”) that executes on a networking device, such as a gateway or router, and performs network traffic control, such as suppressing denial of service attacks or otherwise limiting packet flow. The NCEM performs packet filtering in order to identify and drop packets that are being (or are likely to be) transmitted as part of a denial of service attack. The NCEM conditionally drops packets that meet specified conditions or rules. For example, the NCEM may drop all packets that are using a nonauthentic source address. As another example, the NCEM may limit the volume of packets of a particular type, such as by limiting the number of DNS requests that are made during a specified time interval.

Abstract: Techniques for evaluating a questionable network communication are disclosed. In some implementations, a network of computing systems or devices is provided. Each system includes an evaluation module that determines whether an outbound or inbound network communication is allowable based on one or more factors or properties of the communication, including one or more of an IP address, a listening port, a geographic location, time of day, or the like. The systems in the network may be configured to only communicate with other devices that are identified in a white list of trusted computing systems.

Abstract: Techniques for evaluating a questionable network communication are disclosed. In some implementations, a network of computing systems or devices is provided. Each system includes an evaluation module that determines whether an outbound or inbound network communication is allowable based on one or more factors or properties of the communication, including one or more of an IP address, a listening port, a geographic location, time of day, or the like. The systems in the network may be configured to only communicate with other devices that are identified in a white list of trusted computing systems.

Abstract: Techniques for network security are disclosed. In some implementations, an evaluation module determines whether a network communication from a source computing system to a destination computing system is allowable. The allowability of the communication is determined based properties of the network communication, such as a source or destination address, a port number, a time of day, a geographic location, and the like. If the communication is disallowed, the evaluation module or a related component redirects the communication to an alternative computing system that masquerades as the destination communication system.

Abstract: Techniques for network security are disclosed. In some implementations, an evaluation module determines whether a network communication from a computing device is allowable. The allowability of the communication is determined based on (1) whether the computing device is using an authorized source network address, and (2) whether a non-modifiable identifier of the computing device is authorized. The non-modifiable identifier is a fixed hardware identifier of the computing device, such as an identifier of a CPU, network interface card, storage device, or the like.

Abstract: Techniques for network traffic filtering and flow control are disclosed. Some implementations provide a network communication evaluation module (“NCEM”) that executes on a networking device, such as a gateway or router, and performs network traffic control, such as suppressing denial of service attacks or otherwise limiting packet flow. The NCEM performs packet filtering in order to identify and drop packets that are being (or are likely to be) transmitted as part of a denial of service attack. The NCEM conditionally drops packets that meet specified conditions or rules. For example, the NCEM may drop all packets that are using a nonauthentic source address. As another example, the NCEM may limit the volume of packets of a particular type, such as by limiting the number of DNS requests that are made during a specified time interval.

Abstract: Embodiments enable a database management system (DBMS) to manage two levels of disk failure groups. These two levels of redundancy are achieved by grouping the disks of the disk group for the DBMS into two levels of failure groups (i.e., “data sites” each containing two or more “failure groups” of disks). This system of disk grouping allows a DBMS to potentially tolerate the loss of both an entire first site and part of a second site. Such a DBMS uses a multi-level voting system, based on both failure group-level votes and site-level votes, to identify the current version of administrative data structures (ADS) that store key administrative data. In addition to data sites that store database data, the DBMS includes a quorum site with a single quorum failure group that stores a copy of the ADS. The quorum site contributes a site-level vote during a multi-level voting event.

Abstract: Techniques for evaluating a questionable network communication are disclosed. In some implementations, an evaluation module determines whether a network communication is allowable based on one or more factors, including the listening port, a geographic location, time of day, or the like. In some cases, utilization of a listening port may be limited, such as by restricting the number of network connections that can be opened via the listening port.

Abstract: Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Abstract: Identifying a questionable network address from a network communication. In an embodiment, a network device receives an incoming or outgoing connection request, a web page, an email, or other network communication. An evaluation module evaluates the network communication for a corresponding network address, which may be for the source or destination of the network communication. The network address generally includes an IP address, which may be obtained from the payload section of a network packet. The evaluation module determines one or more properties of the network communication, such as time of day, content type, directionality, or the like. The evaluation module then determines whether the properties match or are otherwise allowed based on properties specified in the white list in association with the IP address.

Abstract: A one-piece paper container is formed by cutting and pressing a single-layer coated paper into an unfolded container body and cover, then steaming to soften one of the surfaces thereof and thermoforming with an upper and lower moldings at 120° C.˜160° C., combining a coating film covering on a plurality of coincided surfaces at the corners and forming enhanced surfaces with protrusions at on the container body and the cover in the meantime, therefore forming a strong structure. Finally, conducting a moisture examination to decide if the product is qualified or not, so as to ensure safety for the consumers.

Abstract: Techniques for evaluating a questionable network communication are disclosed. In some implementations, an evaluation module determines whether a network communication is allowable based on one or more factors, including the listening port, a geographic location, time of day, or the like. In some cases, utilization of a listening port may be limited, such as by restricting the number of network connections that can be opened via the listening port.

Abstract: Identifying a questionable network address from a network communication. In an embodiment, a network device receives an incoming or outgoing connection request, a web page, an email, or other network communication. An evaluation module evaluates the network communication for a corresponding network address, which may be for the source or destination of the network communication. The network address generally includes an IP address, which may be obtained from the payload section of a network packet. The evaluation module determines one or more properties of the network communication, such as time of day, content type, directionality, or the like. The evaluation module then determines whether the properties match or are otherwise allowed based on properties specified in the white list in association with the IP address.