Abstract: A content recommendation method and system based on psychological factors from a consumption profile of a user which is applied in a website for downloading contents through a mobile telephone communications network includes detecting the consumption profile of the user that accesses the website for downloading contents, using a psychographics driver to calculate a psychological profile of the user according to five super traits of a Big Five model, the psychological profile being stored for use in a current query and subsequent queries, and cross-matching the psychological profile and a content data matrix, predefined in a content database, and obtaining as a result of the cross-matching a preferred access of the user to those contents which best adapt to the psychological profile of the user.

Abstract: The invention relates, in an embodiment, to a method for handling a received document. The method includes receiving a plurality of text document samples. The method includes training, using a plurality of text document samples, to obtain a set of machine learning models. Training includes generating fundamental units from the plurality of text document samples for charsets of the plurality of text document samples. Training includes extracting a subset of said fundamental units as feature lists and converting the feature lists into a set of feature vectors. Training further includes generating the set of machine learning models from the set of feature vectors. The method includes applying the set of machine learning models against a set of target document feature vectors converted from the received document. The method includes decoding the received document to obtain decoded content of the received document based on at least the first encoding scheme.

Abstract: A method for creating a risk estimate for a tool includes creating a plurality of source patterns from tool data and maintenance data related to a plurality of tools. The method also includes creating a risk model from the plurality of source patterns, the risk model including a plurality of example stressors each having an associated risk value. The method also includes creating at least one stress pattern from tool data related to the tool and comparing the at least one stress pattern to the risk model to create a risk estimate for the tool.

Abstract: In one advantageous embodiment, a method is provided for managing data. Information about the plurality of components is identified using data to form a plurality of instances for a plurality of nodes in response to receiving data for a plurality of components. Each node in the plurality of nodes corresponds to a component in the plurality of components. A number of variables having a range of values are formed to form a data structure. Each value in the range of values indicates a point in time at which a change to the information for a node occurs between a first instance and a second instance in the plurality of instances. More than one change between two consecutive instances is absent. The data structure is used to identify probability information about the plurality of components associated with the plurality of nodes.

Abstract: Disclosed are systems, apparatuses, and methods for clustering data. Such a method includes providing input data to each of a plurality of cluster microcircuits of a neural network, wherein each cluster microcircuit includes a mean neural group and a variance neural group. The method also includes determining a response of each cluster microcircuit with respect to the input data. The method further includes modulating the mean neural group and the variance neural group of each cluster microcircuit responsive to a value system.

Abstract: Content items, such as e-books, audio files, video files, etc., may be recommended to a user based on the user's presence at a geolocation or venue. Geolocation is the geospatial location of the user, while a venue is a designated area for an activity, such as a concert hall, aircraft, waiting room, etc. The recommendations may be of content items relating to the geolocation or venue, or they may be content items being accessed by others who are, or have been, in approximately the same geolocation or venue.

Abstract: Methods and apparatus for predicting time spans for mobile platform activation are presented. A machine-learning service executing on a mobile platform receives feature-related data. The feature-related data includes usage-related data about time spans that the mobile platform is activated and platform-related data received from the mobile platform. The usage-related data and the platform-related data can differ. The machine-learning service determines whether the machine-learning service is trained to perform machine-learning operations related to predicting a time span that the mobile platform will be activated. In response to determining that the machine-learning service is trained, the machine-learning service: receives a request for a predicted time span that the mobile platform will be activated, determines the predicted time span by the machine-learning service performing a machine-learning operation on the feature-related data, and sends the predicted time span.

Abstract: A system for solving a dynamic constraint satisfaction problem comprises a constraint network of variables and constraints. The system creates a first sub-problem model that includes a first model type, one or more first variables and zero or more first constraints. The system propagates the first constraints through the constraint network and determines if a first conflict is detected from propagating the first constraints. If the first conflict is detected, the system restores the constraint network variables to a first previous state before the first constraints were propagated. The system creates a first sub-problem set that includes a second model type and one or more sub-problem models. The system connects the first sub-problem model to the first sub-problem set via a second constraint and propagates the second constraint through the constraint network.

Abstract: Described is a technology in which a finite state machine-based method segments original sequential data into high level units. Segments with similar sub-sequences are treated as the same unit. In general, the finite state machine indentifies sub-sequences in the sequential data that have similar tree-like microstructures. One described finite state machine operates by entering a path state when an input is a path command corresponding to a non-leaf node; the state machine remains in the path state until another command comprises an action command corresponding to a leaf node, whereby it outputs an action unit, or until the other command comprises a path command that is not the parent or the sibling of a next command, whereby a browsing unit is output. Also described is aggregating sub-sequences of a same unit to produce an access structure corresponding to the unit.

Abstract: A data classification apparatus for classifying plural input data into plural categories, in which the apparatus includes a prototype select unit for selecting the prototype of the category nearest to the input data that has been read, a prototype evaluation unit for evaluating whether the selected prototype is proper, a prototype addition unit for adding a prototype in the case where the selected prototype is not proper and an internal data correcting unit for correcting at least one of the prototype and an area determining parameter specifying the size of the category area for each category in the case where the selected prototype is proper. The size of the category area can be set for each category, and therefore, the data can be properly classified and the judgment accuracy is improved in an application to fault detection and fault diagnosis.

Abstract: In one embodiment, when executing data compression or decompression for a data set, a particular compression category of the data set is determined, and a corresponding probability table specific to the particular compression category of the data set is accessed. Then, one of either arithmetic coding (e.g., an encoder device) or decoding (e.g., a decoder device) may be performed on the data set based on the specific probability table. Specifically, in one or more other embodiments, techniques may statistically generate probability tables specific to particular compression categories.

Abstract: A method for the automatic palletizing of stable package stacks includes the steps of virtual generation of multiple follow-up configurations by adding packages to at least one initial configuration, assessment of these follow-up configurations, and pursuit of follow-up configurations that are assessed as good as initial configurations. These steps are repeated until a termination criterion is satisfied. The follow-up configurations are assessed on the basis of different partial stack heights, towers and/or overbuildings. Another version of the method includes the steps of virtual generation of a package stack, and determination of a characteristic stability value of a package of a layer of a virtual package stack on the basis of the characteristic stability value of packages on which the package rests.

Abstract: A method and apparatus for computer file system traversal. An embodiment of a method includes obtaining a computer archive, the computer archive including one or more data elements. The computer archive is traversed, with traversing the computer archive including processing a data element of the computer archive. Processing a data element includes reading a name of the data element, and determining whether to process the first data element based on the name of the first element. If the first data element is to be processed, the data element is extracted and traversal of the first data element continues. If the data element is not to be processed, the data element is skipped. A serial data stream representing selected data elements of the computer archive is output.

Abstract: Embodiments are directed to interpreting declarative program types at runtime without compiling and mapping between a declarative type and a dynamic runtime type. A computer system accesses a portion of a declarative program, where the declarative program includes fully modeled activity types. The computer system dynamically constructs a dynamic activity type based on one of the fully modeled activity types of the declarative program, where the dynamic activity type is configured for interpretive execution without compilation. The computer system also interpretively executes the dynamically constructed dynamic activity type such that the dynamic activity is executed without compilation.

Abstract: A system, method and computer program product for producing spike-dependent plasticity in an artificial synapse is disclosed. According to one embodiment, a method for producing spike-dependent plasticity in an artificial neuron comprises generating a pre-synaptic spiking event in a first neuron when a total integrated input to the first neuron exceeds a first predetermined threshold. A post-synaptic spiking event is generated in a second neuron when a total integrated input to the second neuron exceeds a second predetermined threshold. After the pre-synaptic spiking event, a first pulse is applied to a pre-synaptic node of a synapse having a phase change memory element. After the post-synaptic spiking event, a second varying pulse is applied to a post-synaptic node of the synapse, wherein current through the synapse is a function of the state of the second varying pulse at the time of the first pulse.

Abstract: A system, method, and design structure for address-event-representation network simulation are provided. The system includes a hardware structure with a plurality of interconnected processing modules configured to simulate a plurality of interconnected nodes. To simulate each node, the hardware structure includes a source table configured to receive an input message and identify a weight associated with a source of the input message. The hardware structure also includes state management logic configured to update a node state as a function of the identified weight, and generate an output signal responsive to the updated node state. The hardware structure further includes a target table configured to generate an output message in response to the output signal, identify a target to receive the output message, and transmit the output message. The hardware structure may further include learning logic configured to combine information about input messages and generated output signals, and to update weights.

Abstract: Methods, systems, and apparatus, including computer program products, for generating or using augmentation queries. In one aspect, statistical model of statistical data is used to support lossless predictive compression. Data instances are identified in statistical data and classified into one of a plurality of data types. Each data type is associated with a corresponding compression process that is used to compress data instances of that type.

Abstract: One or more measurement signatures are derived from a knowledge base of casual biological facts, where a signature is a collection of measured node entities and their expected directions of change with respect to a reference node. The knowledge base may be a directed network of experimentally-observed casual relationships among biological entities and processes, and a reference node represents a perturbation. A degree of activation of a signature is then assessed by scoring one or more “differential” data sets against the signature to compute an amplitude score. The amplitude score quantifies fold-changes of measurements in the signature. In one particular embodiment, the amplitude score is a weighted average of adjusted log-fold changes of measured node entities in the signature, wherein an adjustment applied to the log-fold changes is based on their expected direction of change. In an alternative embodiment, the amplitude score is based on quantity effects.

Abstract: A method for optimizing the scanning process of a mobile terminal, the scanning process being performed by the mobile terminal in order to discover an available network to which a connection is possible, wherein at least a part of existing networks is registered to a server, wherein the server provides its information regarding the registered networks to the mobile terminal, is characterized in that the scanning period of the mobile terminal is dynamically adjusted on the basis of information provided by the server.

Abstract: In operating a communications network, a set of derived events occurs in dependence on at least one primary event in the communications network. A learning method for determining the set of derived events by analyzing the at least one primary event and by predicting the set of derived events based on relations concerning network entities and/or events in the communications network is implemented to support the management of the communications network in a more efficient way.