Abstract: A technique for verifying the safety of tenant data in a data center includes creating a topological map of storage constructs used for storing the tenant data within the data center. The topological map includes a logical storage device, a physical storage device, and a set of hardware structures disposed between the logical and physical storage devices. The constructs of the topological map are evaluated to generate an individual assessment of fault tolerance of each construct, and a set of rules are applied to generate an overall assessment of the safety of the tenant data. In an example, an administrator may operate this technique on a tenant application and generate a report for the tenant summarizing the results.

Abstract: A method, computing system, and one or more computer-readable storage media for clustering short answers to questions are provided herein. The method includes receiving, at a computing device, a number of short answers to a question from a number of remote computing devices. The method also includes automatically grouping the short answers into a number of clusters based on features corresponding to the short answers using a specified clustering technique.

Abstract: Designing a test bed for testing a computer product includes (1) obtaining customer configuration data describing different system configurations in which the computer product is used, (2) preprocessing the customer configuration data to generate structured customer configuration, (3) applying a classification to the structured customer configuration data to separate the distinct system configurations into groupings according to similarity in one or more of the system attributes among members of each group, and (4) for each grouping, identifying a representative system configuration to be used for testing and generating a corresponding set of test bed definition data describing the representative system configuration to enable implementation. The classification may employ clustering analysis, and supplementary and/or backup techniques may be included.

Abstract: A system and method infer true labels for multiple items. The inferred labels are generated from judgments. Multiple judges select the judgments from a specified choice of labels for each item. The method includes determining a characterization of judge expertise and item difficulties based on the judgments. The method also includes determining, using maximum entropy, a probability distribution over the specified choice of labels for each judge and item, based on the judgments. The method further includes selecting improved labels for the items from the specified choice such that the entropy over the probability distribution is reduced. The improved labels represent an improvement from the judgments toward the true labels. Additionally, the method includes performing iterative procedure to determine the true labels, the characterizations of judge expertise and the labeling difficulties.

Abstract: A system and method infer true labels for multiple items. The inferred labels are generated from judgments. Multiple judges select the judgments from a specified choice of labels for each item. The method includes determining a characterization of judge expertise and item difficulties based on the judgments. The method also includes determining, using maximum entropy, a probability distribution over the specified choice of labels for each judge and item, based on the judgments. The method further includes selecting improved labels for the items from the specified choice such that the entropy over the probability distribution is reduced. The improved labels represent an improvement from the judgments toward the true labels. Additionally, the method includes performing iterative procedure to determine the true labels, the characterizations of judge expertise and the labeling difficulties.

Abstract: Systems and methods for providing organized content are described herein. In one example, a method includes identifying a spine document from a collection of documents, wherein the spine document comprises a plurality of sections. The method also includes splitting a related document into a plurality of subdocuments. In addition, the method includes mapping the subdocuments to corresponding sections of the spine document. Furthermore, the method includes displaying subdocuments based on a search of the collection of documents.

Abstract: An overwhelming number of articles are available everyday via the internet. Unfortunately, it is impossible to peruse more than a handful, and it is difficult to ascertain an article's social context. The techniques disclosed herein address this problem by harnessing implicit and explicit contextual information from social media. By extracting text surrounding a hyperlink to an article in a post and assessing the article as a function of content surrounding the hyperlink, an article's social context is determined and presented. Additionally, articles that are sufficiently similar in content may be grouped to establish a many-to-one relationship between posts and an article, creating a more accurate assessment.

Abstract: One or more automatically generated questions regarding subject matter of a body of text are presented (e.g., displayed) to a user. A user input of one or more answers to the one or more automatically generated questions is received, and the body of text is presented to the user, adapted based on a correctness of the one or more answers. The body of text is adapted to emphasize portions of the body of text that are estimated as not having been mastered by the user based on estimated probabilities of user mastery of the various portions of the body of text generated based on the correctness of the one or more answers.

Abstract: The gaming and linguistic data generating technique described herein provides an online multiplayer game that can generate linguistic data, such as, for example, monolingual paraphrase data or multilingual parallel data, as a by-product of the game. The game is designed along the lines of sketch-and-convey paradigm. The game can be played as follows. A phrase is chosen from a phrase corpus and is given to one player (the “Drawer”) who then conveys it to the other player (the “Guesser”) by drawing a picture of the phrase. The Guesser guesses at the components of the phrase either in the same language as the phrase or possibly in a different language. If the Guesser's guesses converge to the chosen phrase, this generates monolingual paraphrases (if the game is played in the same language), and parallel text (if the game is played between multilingual players or two monolingual players in different languages).

Abstract: Assisted clustering systems and methods are described herein that provide a user interface by which a user can easily create clusters and selectively associate data items with such clusters. Information regarding data item-cluster associations made by the user is processed by a recommendation engine to learn a clustering model. The clustering model is then be used to generate recommendations for the user regarding which unassociated data items should be associated with which clusters. In certain embodiments, after the user has determined that the clustering model is performing at a satisfactory level based on the quality of the recommendations, the user can cause the system to automatically cluster a large quantity of remaining unassociated data items. In accordance with further embodiments, a user can specify arbitrary data item types for clustering as well as features of such data types that should be considered in generating the clustering model.

Abstract: The discussion relates to actively teaching a classification boundary. One implementation can obtain examples and a boundary associated with an operational space. This implementation can choose an active teaching strategy to teach the boundary to a user as a classification task. It can select an individual example for presentation to the user utilizing the active teaching strategy. The implementation can receive a user response to the example and evaluate the active teaching strategy in light of the user response.

Abstract: Technology is described for diagnosing problem causes in complex environments by using factorization of a plurality of features. An embodiment can include the operation of identifying a plurality of entities having entity weighting parameters. The entities may be computing devices. The plurality of features can be associated with a respective entity having feature weighting parameters, and an instance of the plurality of features can be associated with individual entity instances. A fault label can be applied for an ensemble entity. The plurality of features can be linked using the feature weighting parameter and the entity weighting parameter with a bilinear model. A further operation is estimating weighting values for the entity weighting parameters and the feature weighting parameters for use in a statistical model. The meaningful feature parameters can be found for the statistical model that are likely to be responsible for entity faults.

Abstract: After an initial training session, a “Dynamic Echo Canceller” (DEC) provides echo cancellation where only access to an input signal and a composite output signal are available, and the input signal is subjected to an unknown variable gain function. In one embodiment, the DEC uses echo cancellation to provide a “clean” copy of a second input signal where only a first input signal and a composite of the first and second input signal is available. An example is a “black box” amplifier coupled to a microphone and a phone line, with access to only the microphone input and a combined output signal where it is desired to retrieve a clean copy of a remote caller signal from the combined output. The DEC is applicable to many fields, including: signal separation; cancellation of echoes caused by impedance mismatches, periodic electrical noise, acoustic echoes caused by acoustic coupling, etc.

Abstract: In one embodiment, a physical world tracking mechanism may monitor the efficacy of an advertisement with an offline conversion component. A data storage device 306 may store a commercial location 110 described in the advertisement and associate a conversion action with the advertisement. A processor 304 may register the conversion action at the commercial location 110 executed by a handheld computing device 104 of a user.

Abstract: Assisted clustering systems and methods are described herein that provide a user interface by which a user can easily create clusters and selectively associate data items with such clusters. Information regarding data item-cluster associations made by the user is processed by a recommendation engine to learn a clustering model. The clustering model is then be used to generate recommendations for the user regarding which unassociated data items should be associated with which clusters. In certain embodiments, after the user has determined that the clustering model is performing at a satisfactory level based on the quality of the recommendations, the user can cause the system to automatically cluster a large quantity of remaining unassociated data items. In accordance with further embodiments, a user can specify arbitrary data item types for clustering as well as features of such data types that should be considered in generating the clustering model.

Abstract: Described is a technology by which a user pastes selected data into a command line of a program, including when the selected data is non-textual. Upon detecting the paste (or drop) action, a variable name is automatically generated and inserted at the current point in a command line, where it acts as a proxy for the pasted data itself. A data structure comprising the selected data or transformed data corresponding to that selected data is maintained in program storage, e.g., RAM allocated to the program. In one aspect, a handler may be used to transform the data from one format into another that may be used by a particular program. For example, text may be reformatted into an array on which the program operates. The handler may be selected from a plurality of possible handlers, including customized handlers.

Abstract: Technology is described for diagnosing problem causes in complex environments by using factorization of a plurality of features. An embodiment can include the operation of identifying a plurality of entities having entity weighting parameters. The entities may be computing devices. The plurality of features can be associated with a respective entity having feature weighting parameters, and an instance of the plurality of features can be associated with individual entity instances. A fault label can be applied for an ensemble entity. The plurality of features can be linked using the feature weighting parameter and the entity weighting parameter with a bilinear model. A further operation is estimating weighting values for the entity weighting parameters and the feature weighting parameters for use in a statistical model. The meaningful feature parameters can be found for the statistical model that are likely to be responsible for entity faults.

Abstract: A graphical user interface for facilitating generation of an accompaniment for a recorded audio melody is described. A Hidden Markov Model, trained with blended chord transition matrices and melody observation matrices, is used for providing the accompaniment for the recorded audio melody. The recorded audio melody includes segments. Frequency analysis of the recorded audio melody is performed. The total duration of each fundamental frequency within a segment of the melody is summed. Based on the summing, a probability for each possible chord for each segment is computed. Based on the computed probabilities, a set of chords are selected for the segments. The chords are displayed on a chord chart of the graphical user interface. The graphical user interface facilitates various manipulations using the chords and/or controls, and generation of a new accompaniment for a recorded audio melody based on the manipulations.

Abstract: A method and system, are provided herein, for automatic selection of one or more items (e.g., files, emails, etc.) in a user interface, based upon selection data received from a user (hard labels) and possibly also predicted (soft) labels (e.g., from a label prediction algorithm) based on behavior data associated with the selections. More particularly, a user selects and deselects items as positive and negative examples that are used as explicit labels to form an item selection list. The explicit labels are used possibly along with additional, predicted labels to create a list of generated items the user may want to select. This list is returned to the user interface as automatically selected items. In one example, the selection list may be iteratively updated upon receiving subsequent user selection data (e.g., user may select items incorrectly selected) by determining alternative items that the user may be interested in selecting.

Abstract: Relating higher-level descriptive musical metadata to lower-level musical elements to enable creation of a song map, song model, backing track, or the like. The musical elements are queried based on input metadata to create a set of musical elements of varying types such as notes, chords, song structures, and the like. The set of musical elements is provided to a user for selection of particular musical elements The selected musical elements represent the song model.