Abstract: A computer-implemented method, computer program product, and computer system for selecting strangers for information spreading on a social network. For the strangers who are users of the social network and not related to each other, information spreading probabilities based on features, information reach, and information spreading probabilities based on a wait time are computed. Fitness scores of the strangers are computed; the fitness scores are a function of the information spreading probabilities, the information reach, and the information spreading probabilities. The strangers are ranked, based on the fitness scores, in a sorted set. One or more of the strangers for the information spreading are selected from the sorted set. The one or more of the strangers for the information spreading are selected by determining an interval in the sorted set, and the interval satisfies an optimization objective of minimizing time for the information spreading within a confidence probability.

Abstract: The present description refers to a computer implemented method, computer program product, and computer system to receive a list of computer events, each of the events including a user interacting with a computer, determine one or more desktop operations, with each desktop operation including one or more computer events that are performed to accomplish the desktop operation, determine one or more knowledge actions, where each knowledge action includes a group of one or more desktop operations performed using a same application on a same information object, determine one or more tasks, with each task including a group of knowledge actions that include information objects that are semantically related, and combine two or more tasks that are temporally related.

Abstract: A method for comparing decision options includes storing a set of decision options. The method includes determining for each decision option of the set of decision options, a plurality of factors, wherein each of the plurality of factors defines a respective metric. The method includes receiving a plurality of user preference indicators, each indicator corresponding to a respective factor of the plurality of factors. The method includes ranking the set of decision options in response to receiving a first user preference indicator. The method includes providing the user with a ranked list of the set of decision options in response to receiving the first user preference indicator. The method includes reranking the set of decision options in response to receiving a second user preference indicator and providing the user with a reranked list of the set of decision options in response to receiving the second user preference indicator.

Abstract: Embodiments of the invention relate to technology prediction. A technical dictionary of technical terms is constructed based on a collection of documents. The technical terms are partitioned into equivalence classes. A table is generated that correlates technical terms across equivalence classes based on temporal co-occurrence of the technical terms across the equivalence classes. For a given technical term the table is accessed to determine a first set of technical terms that correlate to the given technical term. The table is accessed again to determine a second set of technical terms that correlate to the first set of technical terms. It is predicted that the second set of technical terms will correlate to the given technical term in the future.

Abstract: Technologies pertaining to computing a tiering policy that defines how digital items are desirable stored across a plurality of different storage tiers are described herein. A data repository that comprises data that is indicative of historic user interaction with a search engine is accessed. Subsequently, a tiering policy for digital items that are retrievable by way of the search engine is computed based at least in part upon the data that is indicative of the historic user interaction with the search engine. Retrieval times for digital items in the data storage tiers differ across the data storage tiers.

Abstract: The present disclosure provides for categorization of lists of words. The method comprises querying DBpedia to find the resources related to the given list of words. Once the resources are found, the corresponding media Wikipedia categories can be retrieved, as well as their ancestors, generating a graph of categories. A number of graph analysis algorithms can then be applied to the graph, each returning a selected category. For each algorithm a classifier is trained to decide whether the output of the algorithm is indeed the “best” category. An ensemble weighted majority voting can then be used to select the best category based on votes cast by each classifier. The disclosure demonstrates a more accurate selection of the best category and can include an ensemble majority rated voting algorithm comprising all voting members initially casting one vote; i.e., highest frequency, most frequently occurring word, least common ancestor and centrality measures.

Abstract: A threshold learning control system for learning a controller of a robot. The system includes a threshold learning module, a regime classifier, and an exploratory controller, each receiving sensory inputs from a sensor system of the robot. The regime classifier determines a control regime based on the received sensor inputs and communicates the control regime to the threshold learning module. The exploratory controller also receives control parameters from the threshold learning module. A control arbiter receives commands from the exploratory controller and limits from the threshold learning module. The control arbiter issues modified commands based on the received limits to the robot controller.

Abstract: Disclosed are various embodiments for detecting unexpected behavior. A path associated with a user is tracked. It is determined whether the path corresponds to at least one intent grouping that in turn corresponds to an expected behavior. Unexpected behavior is identified when the path does not correspond to at least one intent grouping.

Abstract: A memristor apparatus comprising a plurality of meta-stable switching elements.

Abstract: Semantic information that describes data sets is inferred based upon a semantic analysis performed on data sets retained within a data repository. The semantic analysis can include a determination of formats associated with fields of the data sets and a comparison of values of the fields against reference data sets having predetermined semantic types. Correlations are inferred between data sets based upon respective semantic information. The correlations are incorporated into visualizations displayed in connection with a graphical user interface.

Abstract: A method, executed by a computer system, or altering a base expression associated with an ontology, the method including: generating a canonical expression from the base expression; modifying the canonical expression to generate a modified canonical expression, the modification being the addition, replacement or deletion of a target update expression segment; modifying the base expression to generate a modified base expression by adding a modification sub-expression to the base expression or deleting a modification sub-expression from the base expression, the modification sub-expression being determined at least in part from the target update expression segment; generating a comparison canonical expression from the modified base expression; and comparing the comparison canonical expression with the modified canonical expression to determine whether to substitute the base expression with the modified base expression.

Abstract: A method and system for assisting with guiding a vehicle over terrain is provided. The method includes training at least one first classifier technique using a first set of terrain classifier training data, such that the at least one first classifier technique is trained to output at least one probability value usable to classify terrain. The first trained classifier technique is then used to generate a second set of terrain classifier training data. A second classifier technique is trained using the output of the at least one first classifier technique, and additional data to output a probability value useable to classify terrain.

Abstract: Embodiments of the invention relate to mapping neural dynamics of a neural model on to a lookup table. One embodiment comprises defining a phase plane for a neural model. The phase plane represents neural dynamics of the neural model. The phase plane is coarsely sampled to obtain state transition information for multiple neuronal states. The state transition information is mapped on to a lookup table.

Abstract: A method for performing relational analysis of parsed input is employed to create a visual map of knowledge information. A title, header or subject line for an input item of information is parsed into syntactical components of at least a subject component and any predicate component(s) relationally linked as topic and subtopics. A search of topics and subtopics is carried out for each parsed component. If a match is found, then the parsed component is taken as a chosen topic/subtopic label. If no match is found, then the parsed component is formatted as a new entry in the knowledge map. A translation function for translating topics and subtopics from an original language into one or more target languages is enabled by user request or indicated user preference for display on a generated visual map of knowledge information.

Abstract: A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCI) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller.

Abstract: The present technology concerns techniques for analyzing a legacy system based on at least one trail. The at least one trail includes multiple instances of screens of the legacy system. Plural pair-wise distance measures of the screen instances in the at least one trail are calculated. The pair-wise distance measures indicate a similarity of the respective pair of screen instances that indicates that the pair may originate from the same screen of the legacy system. At least one cluster analysis algorithm is applied on the plural pair-wise distance measures to group the screen instances into at least one screen group, and a map of the legacy system is built based on the at least one screen group.

Abstract: A configuration model dividing and configuration sub-model inference processing system and procedure addresses the issue of configuration model and query complexity by breaking a configuration problem down into a set of smaller problems, solving them individually and recombining the results into a single result that is equivalent to a conventional inference procedure. In one embodiment, a configuration model is divided into configuration sub-models that can respectively be processed using existing data processing resources. A sub-model inference procedure provides a way to scale queries to larger and more complicated configuration models. Thus, the configuration model dividing and configuration sub-model processing system and inference procedure allows processing by a data processing system of configuration models and queries whose collective complexity exceeds the complexity of otherwise unprocessable conventional, consolidated configuration models and queries.

Abstract: In certain implementations, a system may receive attribute data corresponding to attributes of a plurality of users and to one or more venues for which the plurality of users has an affinity. A user personality matrix may be calculated for one or more of the plurality of users based on interrelational nodal link strengths between the one or more users and the venues. The user personality matrices may be merged to calculate a combined personality matrix representing a unified taste profile for the one or more users. A candidate list of venues having the highest link strength with the combined personality matrix may be determined. One or more recommended venues from the candidate list of venues that have the strongest links to the combined personality matrix may be determined, and recommendation data corresponding to the recommended venues may be output.

Abstract: A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Abstract: In one embodiment, a dynamic graph having a plurality of nodes is modeled with a Binary Decision Diagram (BDD). Each pair of nodes in the dynamic graph is modeled using a characteristic function, g({right arrow over (t)};{right arrow over (a)};{right arrow over (b)}), where: {right arrow over (t)} denotes a time; {right arrow over (a)} denotes a first node identifier; {right arrow over (b)} denotes a second node identifier; and g evaluates to 1 (or TRUE) if and only if an edge exists and connects nodes {right arrow over (a)} and {right arrow over (b)} at time {right arrow over (t)}. The BDD is a combination of all the characteristic functions corresponding to all unique pairs of nodes in the dynamic graph.