Abstract: Examples relate to predicting wireless access point availability. In one example, a computing device may: generate, for a wireless access point, a mapping for predicting availability of the wireless access point, the mapping specifying: one or more in-range cellular towers to which at least one client device has been connected while the at least one client device was also connected to the wireless access point; one or more border cellular towers to which at least one client device has been connected to i) subsequent to being connected to one of the one or more in-range cellular towers, and ii) while not connected to the wireless access point; and one or more out-of-range cellular towers to which at least one client device has been connected to i) subsequent to being connected to one of the one or more border cellular lowers, and ii) while not connected to the wireless access point.

Abstract: Examples relate to predicting available access points. In one example, a computing device may: obtain a set of training fingerprints, each training fingerprint specifying, for a client device, i) a set of access points, and ii) cellular signal strength measurements for each cellular tower in a set of cellular towers; generate a plurality of classes based on the set of training fingerprints, each class specifying at least one access point, the access points of each class corresponding to the set of access points specified by at least one training fingerprint, and each combination being different from combinations specified by each other class in the plurality of classes; and train a predictive model to receive, as input, an input fingerprint specifying a cellular signal strength measurement for each cellular tower in a set of input cellular towers and produce, as output, at least one of the plurality of classes.

Abstract: In an example, a set of training fingerprints may be access, in which each of the training fingerprints may specify an access point of a plurality of access points to which a mobile device made a successful connection and a cellular signal strength of a cellular tower near the client device when the successful connection was made. An interim model may be generated from the accessed set of training fingerprints, in which the interim model may contain a subset of the information in the set of training fingerprints to enable a destination device to generate a prediction model to predict an availability of an access point. The generated interim model may be transferred to the destination device.

Abstract: In one example in accordance with the present disclosure, a method for user interest and relationship determination may include distributing a first and a second set of pairs to a plurality of data nodes. The method may also include calculating, on a first data node, a probability of a user's interest in a product based on an observable factor and a latent factor and calculating, on second data node, a probability of a likelihood of a relationship between the user and a second user, based on an observable factor and a latent factor. The method may also include determining a most likely interest and a most likely relationship of the user and predicting a potential interest of the user based on the most likely interest and the most likely relationship.

Abstract: A device of performing social prediction in a social network may include a processor and a memory. In an example, instructions stored in the memory and executable by the processor may classify connections of user pairs within the social network into weak ties and strong ties according to tie strength of the connections. During the generation of a social network model, a first model may be set for the weak ties, and a second model may be set for the strong ties. The social network model may be trained to obtain model parameters, and social data of a user may be predicted by using the model parameters and the social network model.

Abstract: In an example, a set of training fingerprints may be accessed, in which each of the training fingerprints specifies an access point of a plurality of access points to which a mobile device made a successful connection, a relative signal strength of the connection to the access point, and a cellular signal strength of a cellular tower near the mobile device when the successful connection to the access point was made. Level counts of the relative signal strengths of the connections to the access points corresponding to the cellular signal strengths of the cellular tower may be cumulated from the set of training fingerprints. The cumulated level counts of the relative signal strengths of the access points may be ranked.

Abstract: In an example, a set of training fingerprints may be access, in which each of the training fingerprints may specify an access point of a plurality of access points to which a mobile device made a successful connection and a cellular signal strength of a cellular tower near the client device when the successful connection was made. An interim model may be generated from the accessed set of training fingerprints, in which the interim model may contain a subset of the information in the set of training fingerprints to enable a destination device to generate a prediction model to predict an availability of an access point. The generated interim model may be transferred to the destination device.

Abstract: The present disclosure relates to a polymer composition. The disclosed composition comprises a polycarbonate polymer, a laser direct structuring additive capable of being activated by electromagnetic radiation and thereby forming elemental metal nuclei, reinforcing filler, and a laser direct structuring synergist. Also disclosed is a method for making the disclosed polymer composition and an article of manufacture comprising the disclosed polymer composition.

Abstract: Examples relate to hidden dynamic systems. In some examples, a conditional probability distribution for labeling data record segments is defined, where the conditional probability distribution models dependencies between class labels and internal substructures of the data record segments. At this stage, optimal parameter values are determined for the conditional probability distribution by applying a quasi-Newton gradient ascent method to training data, where the conditional probability distribution is restricted to a disjoint set of hidden states for each of the class labels. The conditional probability distribution and the optimal parameter values are used to determine a most probable labeling sequence for the data record segments.

Abstract: The disclosure concerns polymer compositions exhibiting thermal conductivity and laser marking properties while maintaining mechanical properties and a light color throughout the composition.

Abstract: Disclosed are platable resin compositions for use in, for example, metal plating of plastics. The resin compositions include polycarbonate, acrylonitrile butadiene styrene and a filler, and may exclude a laser direct structuring additive. The compositions possess markedly improved characteristics, such as notched IZOD impact, flex modulus, and peel strength. Also disclosed are methods for plating metal on a substrate that is formed from the present resin compositions, as well as articles that include the disclosed compositions.

Abstract: Example embodiments relate to scalable web data extraction. In example embodiments, a joint potential function is defined for data record segments of web data extracted from a web page, where the joint potential function models data record segmentation of the web data and dependencies between pairs of data segments in the data record segments. At this stage, a principal record segment and several related record segments are identified from the data record segments, where each of the plurality of related record segments is associated with the principal record segment. A related attribute is determined for each related record segment. Next, the joint potential function is applied to the principal record segment and each corresponding related segment to determine a relationship label that describes a data relationship between the principal record segment and the corresponding related segment.

Abstract: Rank aggregation based on a Markov model is disclosed. One example is a system including a query processor, at least two information retrievers, a Markov model, and an evaluator. The query processor receives a query via a processing system. Each of the at least two information retrievers retrieves a plurality of document categories responsive to the query, each of the plurality of document categories being at least partially ranked. The Markov model generates a Markov process based on the at least partial rankings of the respective plurality of document categories. The evaluator determines, via the processing system, an aggregate ranking for the plurality of document categories, the aggregate ranking based on a probability distribution of the Markov process.

Abstract: Disclosed is a use of flavones derivatives and flavanone derivatives in preparation of sedative and hypnotic drugs.

Abstract: The present disclose provides a method of recommending at least one item to a user, the method comprising: receiving the user's feedback on an item; predicting a user vector and an item vector related to the user in an online mode based on the received feedback; and recommending to the user an acceptable item based on the user vector and item vector.

Abstract: Examples relate to predicting access point availability. In one example, a computing device may: obtain a set of training fingerprints, each training fingerprint specifying, for a client device, an access point to which the client device successfully connected and cellular signal strength for each cellular tower in a set of cellular towers; and for each access point: generate an access point profile for the access point, the access point profile indicating, for each cellular tower in the set of cellular towers specified by a first subset of the set of training fingerprints, a probability that a randomly selected training fingerprint included in the first subset specified a particular cellular signal strength for the cellular tower, wherein each training fingerprint included in the first subset specifies the access point as the access point to which the client device specified by the training fingerprint included in the first subset successfully connected.

Abstract: Relationship extraction can include applying unsupervised relationship learning to a logic knowledge base and a plurality of entity groups recognized from a document to provide a probabilistic model. Relationship extraction can include performing joint inference on the probabilistic model to make simultaneous statistical judgments about a respective relationship between at least two entities in one of the plurality of entity groups. Relationship extraction can include extracting a relationship between at least two entities in one of the plurality of entity groups based on the joint inference.

Abstract: Information extraction from observed data may be performed. First parameter weights and second parameter weights of a joint discriminative probability distribution may be determined. The joint discriminative probability distribution may be over first variables and second variables and may be conditioned on the observed data. The second variables may be modeled by first-order logic formulas. The first variables may be based on the first parameter weights, and the second variables may be based on the second parameter weights. A first likely output of the first variables based on the first parameter weights and a second likely output of the second variables based on the second parameter weights may be determined.

Abstract: Example implementations relate to predicting social actions and social ties of users in a social network. For example, a computing device may include a processor. The processor may access social network data associated with a set of users of a social network. The processor may also parse social action data and social tie data from the social network data and calculate a set of parameters defining relationships between the social action data and the social tie data. The processor may further determine, using the set of parameters, a social action and a social tie associated with a particular user of the social network.

Abstract: Disclosed is a use of flavones derivatives and flavanone derivatives in preparation of sedative and hypnotic drugs.