Abstract: Some examples are directed to determining a frequency of occurrence of a first value in a component of a first plurality of candidate solutions for an optimization problem where the first plurality of candidate solutions has converged on a sub-optimal solution during a computer simulation that tests fitness of the first plurality of candidate solutions. Some examples are further directed to determining a second value that is inversely biased from the frequency of occurrence of the first value. Some examples are further directed to including the second value in a component of at least a portion of a second plurality of candidate solutions, where the component of the at least the portion of the second plurality of candidate solutions corresponds to the component of the first plurality of candidate solutions.

Abstract: Embodiments of the present invention provide a method, system and computer program product for visualizing rule input attributes with a rule according to disparate rule attribute distributions. In an embodiment of the invention, a method for visualizing rule input attributes with a rule according to disparate rule attribute distributions has been provided. The method includes identifying in response to a selection of a rule for viewing in a rule viewer an input for the selected rule and determining an attribute for the input. However, attributes present in a guard for the selected rule can be excluded. The method also can include computing a global distribution of the attribute irrespective of the identified input and a specific distribution for the identified input. The attribute can be flagged as being correlated with the selected rule when it is determined that a sufficient disparity exists between the global distribution and the specific distribution.

Abstract: Embodiments of the present invention provide a method, system and computer program product for visualizing rule input attributes with a rule according to disparate rule attribute distributions. In an embodiment of the invention, a method for visualizing rule input attributes with a rule according to disparate rule attribute distributions has been provided. The method includes identifying in response to a selection of a rule for viewing in a rule viewer an input for the selected rule and determining an attribute for the input. However, attributes present in a guard for the selected rule can be excluded. The method also can include computing a global distribution of the attribute irrespective of the identified input and a specific distribution for the identified input. Thereafter, the attribute can be flagged as being correlated with the selected rule when it is determined that a sufficient disparity exists between the global distribution and the specific distribution.

Abstract: An object tracking system and method operable to minimize processing time for tracking objects is provided. The system includes a pair of filters operable to make associations between dynamic objects newly detected and previously detected. One of the pair of filters makes an association when the predicted location of the previously detected objects is within a predetermined distance of the newly detected object. The other of the pair of filters makes an association based upon the probability that a newly detected dynamic object is a previously detected dynamic object. The remaining unassociated dynamic objects are then localized so as to form discrete matrices for optimization filters.

Abstract: Concepts and technologies are described herein for hyperlocal smoothing. The hyperlocal smoothing solutions described herein provide a smooth view of data and events across hyperlocal geographic areas by combining sparse data available with inferred or extrapolated data. Additionally, the hyperlocal smoothing solutions described herein make use of contextual analysis to interpret service requests in a manner appropriate for a targeted hyperlocal area. Thus, the smooth view of data can be queried in a contextually sensitive manner to return relevant information for a hyperlocal geographic area, even in circumstances wherein data relevant to the hyperlocal geographic area is sparse or even non-existent.

Abstract: An example system and process where some operations include determining that a plurality of values satisfy one or more constraint conditions for an optimization problem. The operations further include randomly selecting a set of one or more values from the plurality of values after determining that the plurality of values satisfy the one or more constraint conditions. The operations further include including the set of one or more values in a candidate solution for the optimization problem. The including the set of one or more values in the candidate solution causes the candidate solution to comply with the one or more constraint conditions for the optimization problem prior to running a computer based simulation for the optimization problem.

Abstract: The program makes a computer function as a significance evaluation unit including a statistical equation storage unit which stores algorithms of a plurality of statistical methods, a significance probability calculation unit which calculates significance probability for each of the evaluation items with the algorithm read out from the statistical equation database based on an input signal indicating one statistical method selected, a significance determination unit which determines a magnitude relationship between the significance probability and a significance level which is previously set or input and gives significance information to each of the evaluation items, and an evaluation result output unit which makes a display device output a matrix table in which the same number of cells as that of the evaluation items are provided such that each cell is corresponded to each evaluation item and cells of n columns or n rows are arranged.

Abstract: An example system and process with some operations that include determining a plurality of constraint compliant values and determining that a candidate solution, which was created from two or more of a plurality of candidate solutions via a crossover operation, fails to comply with a constraint condition for an optimization problem, wherein the two or more of the plurality of candidate solutions comply with the constraint condition. In some examples, the operations further include assigning a value from the plurality of constraint compliant values to a constrained variable of the candidate solution in response to the determining that the candidate solution fails to comply with the constraint condition, wherein the value assigned from the plurality of constraint compliant values is randomly selected from the plurality of constraint compliant values, and wherein the at least one constrained variable is constrained to comply with the constraint condition.

Abstract: A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the connectivity graph based feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the connectivity graph based feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the connectivity graph based feature, and categorizing the data unit as associated with the botnet based on the label.

Abstract: A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time.

Abstract: Various embodiments provide techniques for analyzing resources based on a set of rules to determine problems that may occur during a product (e.g., a software product) development cycle. In accordance with some embodiments, resources can include files, strings, images, text, user interface elements (e.g. controls, radio buttons, and/or windows), and so on. In at least some embodiments, a resource analysis utility (“RAU”) is provided that can load resource files and analyze associated resources according to a set of rules. According to some embodiments, the RAU includes a graphical user interface (“GUI”) that enables resource files to be selected and rules to be selected that are to be used to analyze the resource files. The GUI can also include results of the analysis of the resource files.

Abstract: An example system and process with some operations that include determining a plurality of values, wherein assignment of any of the plurality of values to one or more constrained variables of a candidate solution causes the candidate solution to comply with a constraint condition of an optimization problem, wherein the candidate solution complies with the constraint condition prior to being mutated via a computer-based simulation that tests fitness of the candidate solution. The operations further include after the candidate solution is mutated, determining that the candidate solution fails to comply with the constraint condition. The operations further include assigning at least one value, which is randomly selected from the plurality of values, to the one or more constrained variables of the candidate solution.

Abstract: A method for predictive analytics in a semi-structured process including updating, iteratively, at least one probability of a probabilistic process model based on a completed task, wherein updating the at least one probability of the probabilistic process model includes receiving the probabilistic process model associated with a todo list including a plurality of tasks of the semi-structured process, defining a cost of each of the plurality of tasks, prioritizing the plurality of tasks according to the costs, and recommending a next task from the todo list according to a prioritization

Abstract: A personal taste assessment system receives a person's ratings for various rated items, such as wines or other beverages, and it develops a preference model for the person based on the received ratings. When developing the preference model, the system accesses a database of candidate items, wherein each candidate item is associated with at least one trait value. The system retrieves from the database a value for at least one trait for each of the rated items, identifies at least one pattern of dependency between at least one of the retrieved trait values and at least one of the received ratings, and determines a preference model based on the at least one pattern of dependency. The system may use the preference model to develop a preference profile for the person, make recommendations, and/or make predictions as to items that the person and/or a group that includes the person may enjoy.

Abstract: A context-aware information obtaining method and apparatus uses local service information. A communication apparatus may receive local service information associated with a location of the communication apparatus. The communication apparatus may interpret the local service information to generate context-aware information of the communication apparatus.

Abstract: Aspects of the disclosure provide for detection of spam business listings. Aspects operate to identify business listing characteristics in trusted sources and untrusted sources. As untrusted sources are likely to contain more spam, characteristics that are present in untrusted sources but not present in trusted sources are typically indicative of spam listings, and vice versa. Thus, statistical analysis of the frequency of characteristics within each source may be used to identify common characteristics of spam listings. These characteristics may further be analyzed in specific listing contexts, as different listing contexts (e.g., different types of businesses) typically use different terms and vocabularies, such that terms that are indicative of spam in one context may not be indicative of spam in another. Various methods for leveraging this context-specific statistical information to improve spam detection operations are disclosed.

Abstract: A method to support multi-dimensional tuples in rule engines having rule engine executed by a processing device can evaluate facts in a working memory stored on a data storage device against a rule with repetitions. The rule engine may create a multi-dimensional tuple to hold a set of the facts that matches the rule. The multi-dimensional tuple has a set of elements and an index for each of the elements. Each element of the multi-dimensional tuple is a single fact or a tree structure for holding a set of facts of a repetition group that matches the rule with repetitions.

Abstract: Embodiments provide capacity estimates for components of a computing environment, but are not so limited. In an embodiment, a computer-implemented method includes using residual capacity estimates and smooth averages to provide point-in-time capacity estimates for resources of a computing environment. In one embodiment, a computing environment includes a capacity manager that can operate under processor control to provide capacity estimates for a collection of resources as part of a capacity monitoring and planning process. Other embodiments are included and available.

Abstract: An information processing apparatus is disclosed. The apparatus may include a processing method preparation unit for generating a first processing method. The apparatus may include an evaluator generation unit for generating an evaluator based on a genetic algorithm, using one or more input data sets, each of which may include data and a corresponding evaluation value. The apparatus may include an evaluation unit for calculating, using the evaluator, a first evaluation value using first output data obtained by processing the data using the first processing method. The apparatus may include a processing method update unit for generating a second processing method such that a second evaluation value calculated by the evaluator, using second output data obtained by processing the data using the second processing method, is higher than the first evaluation value. The apparatus may include an output unit that outputs the second output data and the second processing method.

Abstract: A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the feature, and categorizing the data unit as associated with the botnet based on the label.