Abstract: An example system includes a vehicle having a prime mover motively coupled to a drive line; a motor/generator selectively coupled to the drive line, and configured to selectively modulate power transfer between an electrical load and the drive line; a battery pack; a DC/DC converter electrically interposed between the motor/generator and the electrical load, and between the battery pack and the electrical load, the DC/DC converter comprising a DC/DC converter housing; and a covering tray positioned over a plurality of batteries of the battery pack, the covering tray comprising a connectivity layer configured to provide electrical connectivity to terminals of the plurality of batteries.

Abstract: A method for replicating data from a plurality of storage systems to a target storage system. The method implemented at the target storage system receives a replication request to replicate data from a source storage system to the target storage system, where the target storage system stores data replicated from the plurality of source storage systems. In response to the replication request, the target storage system identifies a replication resource limit associated with the data to be replicated from the source storage system. The target storage system then determines whether a replication resource requested by the replication request has exceeded the replication resource limit. The target storage system thus allows the replication resource to replicate data from the source storage system, in response to the replication resource not exceeding the replication resource limit.

Abstract: A system and methods for providing guarantees about faithful execution of databases that run under the control of an untrusted entity—such as a cloud computing service provider—that is different than the owner of the data and applications being outsourced; or runs on a server that may be compromised by unauthorized users. Specifically, the system and methods establishes that an untrusted database is being faithfully hosted and provides indemnity for the service provider from false claims, enabling the server to prove its innocence against untrue claims of faulty operation. The invention also provides assured provenance for all changes made to the database.

Abstract: Techniques to dynamically select data sources for users and to direct queries for the users to the proper data sources. Initially, user information (e.g., user name) for each user is received and used to determine a data source designated for the user. The designated data source may be determined via a look-up table or by running a query with the user information to obtain the identity (e.g., the logical name) of the data source. A data source variable for each user can then be set to the identity of the designated data source. Thereafter, queries for each user are directed to the user's designated data source by determining database connection information for the designated data source based on the data source variable and using this information to direct the queries.

Abstract: A system and methods for providing guarantees about faithful execution of databases that run under the control of an untrusted entity—such as a cloud computing service provider—that is different than the owner of the data and applications being outsourced; or runs on a server that may be compromised by unauthorized users. Specifically, the system and methods establishes that an untrusted database is being faithfully hosted and provides indemnity for the service provider from false claims, enabling the server to prove its innocence against untrue claims of faulty operation. The invention also provides assured provenance for all changes made to the database.

Abstract: Techniques to provide dynamic multi-lingual support for software applications such as analytics, which typically rely on two different types of data, underlying data and metadata, to generate reports. The metadata defines the underlying data and a presentation layer displays the reports, which may be generated using presentation and metadata layer objects. Each such object may be defined by a language independent representation, which may in turn be associated with multiple display or language dependent representations (i.e., translations or translated strings) for multiple supported languages. One of the languages may be selected for use in a given session, and the display representations for the selected language are then bound to the language independent representations. The binding may be dynamically performed and is session-specific.

Abstract: A system and method that provide resilient watermarking of digital data, including numeric and categorical relational data. The method and system are premised upon a framework considering the higher level semantics to be preserved in the original data. The system and method also provides a technique for enabling user-level run-time control over the properties that are to be preserved, as well as the degree of change introduced. The system and method are resilient to various important classes of attacks, including subset selection, addition, alteration, or resorting, horizontal or vertical data partitioning, and attribute remapping. For numeric relational data, approximately 40-45% of the embedded watermark may survive approximately 45-50% of original data loss, while for categorical relational data, the watermark may survive approximately 80% of original data loss with only approximately 25% degradation of the watermark.

Abstract: A system and method that provide resilient watermarking of digital data, including numeric and categorical relational data. The method and system are premised upon a framework considering the higher level semantics to be preserved in the original data. The system and method also provides a technique for enabling user-level run-time control over the properties that are to be preserved, as well as the degree of change introduced. The system and method are resilient to various important classes of attacks, including subset selection, addition, alteration, or resorting, horizontal or vertical data partitioning, and attribute remapping. For numeric relational data, approximately 40-45% of the embedded watermark may survive approximately 45-50% of original data loss, while for categorical relational data, the watermark may survive approximately 80% of original data loss with only approximately 25% degradation of the watermark.