Abstract: Data is received that characterizes a software system. Thereafter, using at least one machine learning model trained using historical testing data from a plurality of training software systems, a recommended computing architecture is generated for the software system. Data can then be provided that characterizes the software system. Related apparatus, systems, techniques and articles are also described.

Abstract: Data is received that characterizes a software system. Thereafter, using at least one machine learning model trained using historical testing data from a plurality of training software systems, a recommended computing architecture is generated for the software system. Data can then be provided that characterizes the software system. Related apparatus, systems, techniques and articles are also described.

Abstract: An example of a method includes identifying a formulated query and at least one past query associated with an input query. The method also includes determining a first similarity score between the input query and the formulated query. Further, the method includes updating the first similarity score based on a second similarity score between the input query and the at least one past query, and based on a third similarity score between the formulated query and the at least one past query. Moreover, the method includes extracting one or more contents based on the formulated query if updated first similarity score is greater than a first threshold. The method also includes providing the one or more contents.

Abstract: An example of a method includes identifying a formulated query and at least one past query associated with an input query. The method also includes determining a first similarity score between the input query and the formulated query. Further, the method includes updating the first similarity score based on a second similarity score between the input query and the at least one past query, and based on a third similarity score between the formulated query and the at least one past query. Moreover, the method includes extracting one or more contents based on the formulated query if updated first similarity score is greater than a first threshold. The method also includes providing the one or more contents.

Abstract: A method for executing multiple, cascading ad auctions, including receiving a request to deliver ads in response to a search query for display on a search results page having a plurality of ad sections; executing at least first and second sequential auctions for the plurality of ad sections by: ranking the ads in the first auction and identifying which of the ads placeable in the North section to deliver according to a first utility function that considers a relevance of the search results page with the ads placed; ranking a remainder of the ads not identified for delivery to the North section in the second auction and identifying which of the remainder of the ranked ads to deliver to a second section based on a second utility function; and delivering the ads identified by the first utility function for display in the North section and the ads identified by the second utility function for display in the second section.

Abstract: A method for reducing ad impact on users in a search results page includes receiving a request to deliver ads in response to a search query for display on a search results page; receiving relevance scores for a plurality of ranked web results that are to be served to the search results page; ranking a plurality of ads identified as relevant to the search query according to a position-normalized, click-through-rate metric and bid values, wherein a predetermined number of the top-ranked ads are placeable in a plurality of North ad slots; incrementally and additively placing the placeable ads sequentially according to rank (k) in their respective North ad slots until a utility score generated by a utility function for a current iteration of ads fails to exceed a threshold value; and delivering to the search results page the ads placed in the North ad slots.

Abstract: In a method for equalization of a data signal based on the Viterbi algorithm, trellis contributions are first of all calculated for reconstructed signal values from transition metrics, and are stored in a first table memory. If a reduced trellis diagram is intended to be used as the basis for calculation of the transition metrics, decision feedback contributions are also calculated, and are stored in a further table memory. In the case of ACS operations, the first and, if appropriate, the further table memories are accessed in order to determine the reconstructed signal values.

Abstract: A system and method for spoken language proficiency assessment by a computer is described. A user provides a spoken response to a constructed response question. A speech recognition system processes the spoken response into a sequence of linguistic units. At training time, features matching a linguistic template are extracted by identifying matches between a training sequence of linguistic units and pre-selected templates. Additionally, a generalized count of the extracted features is computed. At runtime, linguistic features are detected by comparing a runtime sequence of linguistic units to the feature set extracted at training time. This comparison results in a generalized count of linguistic features. The generalized count is then used to compute a score.

Abstract: In a method for equalization of a data signal based on the Viterbi algorithm, trellis contributions are first of all calculated for reconstructed signal values from transition metrics, and are stored in a first table memory. If a reduced trellis diagram is intended to be used as the basis for calculation of the transition metrics, decision feedback contributions are also calculated, and are stored in a further table memory. In the case of ACS operations, the first and, if appropriate, the further table memories are accessed in order to determine the reconstructed signal values.