Abstract: Monitoring system and method for use with diabetic treatment management. The system includes: a communication interface configured to permit access to stored raw log data, obtained over a certain time, being indicative of glucose measurements, meals consumed and insulin delivery; and a control unit including an unsupervised learning controller configured to receive and process said raw log data and determine at least one global insulin pump setting of basal rate, correction factor, carbohydrate ratio and insulin activity curve parameters. The system may include a processing unit including a first processor for processing measured data indicative of blood glucose level and generating first processed data, a second processor including at least one fuzzy logic module which receives input parameters corresponding to the measured data, the first processed data and a reference data, and processes the data to produce a qualitative output parameter to determine whether any treatment parameter should be modified.

Abstract: A method, apparatus, system, and computer program, for operating a network planning tool. The method includes recognizing at least one predetermined criteria being satisfied or triggered, and learning at least one network attribute, to be used in subsequent network planning. Various types of network attributes can be learned, such as, e.g., network element parameters, site parameters, link parameters, demand parameters, optical parameters, general parameters, and networking parameters. Example attributes may specify, e.g., a maximum number of add/drop channels, at least one alarm threshold, a maximum number of sites per network ring, a maximum number of add/drop sites per ring, and a maximum ring circumference. Others can specify a maximum light path distance, a routing tendency, at least one grooming node, or the like.

Abstract: Methods, systems and computer program products are provided. Methods for detecting helpful query responses include generating helpfulness terminology data corresponding to words, terms, phrases and/or symbols that correspond to an expression of helpfulness. A query based exchange including multiple messages is identified. The messages are analyzed using the helpfulness terminology data to detect a helpful message. The helpful message is associated with a source participant and a helpfulness value that corresponds to the source participant is modified. A value corresponding to the query based exchange indicating that the query based exchange was successful is stored.

Abstract: A non-transitory computer-readable medium tangibly embodied in a storage device encoded with instructions that, when executed on a processor, perform a method in a computer system for testing a rule-driven system, the method comprising detecting a potential read-write error or a potential write-write error in the rule-driven system, generating test results based on the detecting, and reporting the test results.

Abstract: An approach is presented for identifying related problem tickets in an information technology (IT) environment. User interactions with a computer program are stored. The user interactions include inputs to the computer program to search for problem tickets issued in the IT environment that have the same characteristics. One or more user interaction patterns within the user interactions are recognized. A user interaction pattern of the one or more user interaction patterns is selected based on an evaluation of effectiveness of each of the one or more user interaction patterns. Based on the user interaction pattern, a rule is generated for determining which problem tickets in the IT environment share a common characteristic. The rule is applied to additional problem tickets issued in the IT environment to identify which of the additional problem tickets share the common characteristic.

Abstract: The present disclosure introduces a method, an apparatus and memory of predicting a user behavior number of a word for reducing the amount and the complexity of operation, saving the consumption of the equipment, and improving the accuracy and reliability of predictions. In an embodiment, a historical data sequence of the user behavior number of a word is converted from a time domain to a frequency domain. Based on the converted frequency domain, each estimated cycle and its effect rate value of the historical data sequence are ascertained. If the historical data sequence is stable, an average value of user behavior numbers of some historical data points before a prediction point is calculated as a user behavior number of the prediction point. Otherwise, the user behavior number is calculated based on a selected main cycle and a selected singularity.

Abstract: Described are techniques for providing views of an object model. A template is received that specifies allowable access for a first object using qualifiers for the first object. The qualifiers include a first list of one or more roles and a second list of one or more levels of expertise. For a user identifier having an associated role and an associated level of expertise, it is determined whether to allow access to the first object. The determining includes determining whether the associated role is included in the first list and whether the associated level of expertise is included in the second list. Access to the first object is allowed if the associated role is included in the first list and the associated level of expertise is included in the second list.

Abstract: Disclosed are system, method and computer program product for adaptive control of actions of a user on a computer system. The system monitors one or more actions of the user, applies restriction rules to detect prohibited user actions, and blocks prohibited actions that violate at least one restriction rule. The system also collects information on allowed actions of the user and corresponding system events, analyzes in real-time the collected information about system events corresponding to the allowed actions to detect anomalous actions that did not violate any of the restriction rules, but caused abnormal increase in the usage of certain system resources. When an anomalous action is detected, the system identifies restriction rules that are associated with the detected anomalous action and edits these rules or creates new restriction rules to include the anomalous action prohibited to the user.

Abstract: An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

Abstract: A method for machine-learning by analogy, comprising: providing a network of component computer systems, each component having sequential layers, each layer having parallel segments, each segment comprises a processor and a memory store, each memory store is configured to store a domain-specific case base, which is defined by a situation-action pair; stochastically transforming cases in each case base through automatic deterministic generalization and analogy when the corresponding processor is in a dream mode to create transformed cases; providing a user-entered situation; modifying the user-entered situation by expanding its contextual mnemonics; searching each case base for cases and transformed cases that include contextual subsets of the modified, user-entered situation; for a given case base, mapping the modified, user-entered situation to a matched action within the given case base; creating a new case comprising the user-entered situation and the matched action.

Abstract: A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

Abstract: An information extraction system and methods of operating the system are provided. In particular, an information extraction system for performing meta-extraction of named entities of people, organizations, and locations as well as relationships and events from text documents are described herein.

Abstract: An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

Abstract: A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors.

Abstract: A medical digital expert system to predict a patient's response to a variety of treatments (using pre-treatment information) is described. The system utilizes data fusion, advanced signal/information processing and machine learning/inference methodologies and technologies to integrate and explore diverse sets of attributes, parameters and information that are available to select the optimal treatment choice for an individual or for a subset of individuals suffering from any illness or disease including psychiatric, mental or neurological disorders and illnesses. The methodology and system can also be used to determine or confirm medical diagnosis, estimate the level, index, severity or critical medical parameters of the illness or condition, or provide a list of likely diagnoses for an individual suffering/experiencing any illness, disorder or condition.

Abstract: Techniques are described for determining queries that are similar to rare queries. An n-gram space is defined to represent queries and a similarity function is defined to measure the similarities between queries. The similarity function is learned by leveraging training data derived from user behavior data and formalized as an optimization problem using a metric learning approach. Furthermore, the similarity function can be defined in the n-gram space, which is equivalent to a cosine similarity in a transformed n-gram space. Locality sensitive hashing can be exploited for efficient retrieval of similar queries from a large query repository. This technique can be used to enhance the accuracy of query similarity calculation for rare queries, facilitate the retrieval of similar queries and significantly improve search relevance.

Abstract: A computer implemented method is provided to expand a limited amount of input to conditional probability data filling a Bayesian Belief network based decision support apparatus. The conditional probability data defines conditional probabilities of states of a particular network node as a function of vectors of state values of a set of parent nodes of the particular network node in the Bayesian Belief network. The computer implemented method comprises receiving elicited conditional probability data that defines the conditional probabilities of the state values of the particular network node for a subset of all possible vectors of combinations of state values of the parent nodes. Conditional probability data defining the conditional probabilities of the state values of the particular network node for further possible vectors of state values of the parent nodes is interpolated from the elicited conditional probability data. The influence factors are determined.

Abstract: A constraint condition DB storing a constraint condition that stipulates a structure of a decision tree is referenced, and a decision tree is generated from a case set where values of a plurality of attributes and a conclusion are associated with one another so that the structure of the decision tree, which is stipulated by the constraint condition, is satisfied. Accordingly, for example, even if a new case is added to the case set, a basic structure of the decision tree is succeeded by the constraint condition, thereby avoiding a situation where an operator needs to significantly modify the decision tree. Therefore, operations of modifying the decision tree by the operator can be reduced.

Abstract: Methods, systems, and products adapt recommender systems with pairwise feedback. A pairwise question is posed to a user. A response is received that selects a preference for a pair of items in the pairwise question. A latent factor model is adapted to incorporate the response, and an item is recommended to the user based on the response.

Abstract: An operating ability monitoring system that includes a person-dependent acceleration sensor, a telemetry unit, and an evaluation unit, wherein the person-dependent acceleration sensor and the telemetry unit may be worn by and/or implanted in a machine operator and connected to the evaluation unit, and wherein the evaluation unit is configured to receive acceleration values originating from the person-dependent acceleration sensor and sensor values originating from a further sensor that reflects the movements and/or acceleration of a vehicle or moving machine and to evaluate them by comparing the acceleration values from the person-dependent acceleration sensor to the sensor values associated with the vehicle or moving machine.