Abstract: A system having a wearable devices that, together with a cognitive model, are able to analyze a person to determine if they are in the flow and/or guide the person to get into the flow are disclosed. The system and processes help persons to find their unique formula to achieve flow. By using a cognitive AI engine, the system can describe a space of mental states and the actions that cause transitions between them for each individual.

Abstract: A computer program product, an apparatus and a method for training of an HMM. The method comprises applying a classifier that uses an HMM which was trained based on a training set, on a set of samples to provide an initial prediction; computing a first F1-score of the initial prediction measuring an accuracy of the initial prediction; selecting a misclassified sample by the classifier in the initial prediction; adding the misclassified sample to the training set; training the HMM using the misclassified sample to provide a modified HMM; applying the classifier using the modified HMM on the set of samples to provide a second prediction; computing a second F1-score of the second prediction; and comparing the first F1-score and the second F1-score; in response to a determination that the first F1-score is greater than the second F1-score, removing the misclassified sample from the training set.

Abstract: Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.

Abstract: Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.

Abstract: A method for determining a variable near-optimal policy for a problem formulated as Markov Decision Process, the problem comprising at least one limited action entry, the limited action entry being an entry of an action of a finite set of actions limited in the number of times its value may be changed, the method comprising using at least one hardware processor for: receiving data elements with respect to the problem, the data elements comprising: (a) a finite set of states, (b) the finite set of actions, (c) a transition probabilities matrix determining transition probabilities between states of the finite set of states, once actions of the set of actions are performed; (d) an immediate cost function, wherein the value of the immediate cost function is determined for a pair of a state of the finite set of states and an action of the finite set of actions, and (e) a discount factor; updating one or more data elements of the received data elements relating to the at least one limited action entry, wherein the

Abstract: Embodiments of the present invention may provide the capability to detect complex events while providing improved detection and performance. In an embodiment of the present invention, a method for detecting an event may comprise receiving data representing measurement or detection of physical parameters, conditions, or actions, quantizing the received data and selecting a number of samples from the quantized data, generating a hidden Markov model representing events to be detected using initial model values based on ideal conditions, wherein a desired output is defined as a sequence of states, and wherein a number of states of the hidden Markov model is less than or equal to the number of samples of the quantized data, adjusting the quantized data and the initial model values to improve accuracy of the model, determining a state sequence of the hidden Markov model, and outputting an indication of a detected event.

Abstract: A computer-implemented method, computerized apparatus and computer program product for activity recognition using adaptive window size segmentation of sensor data stream. A data stream generated by one or more sensors is obtained. A frequency analysis of the data in a first segment of the data stream is performed. A size of a second segment is determined based on the frequency analysis. Activity recognition is performed for the second segment by extracting one or more features of the data therein and applying a machine learning process on the extracted features to obtain a classification of the data into an activity class.

Abstract: According to an aspect of some embodiments of the present invention there is provided a computer implemented method of automatically generating and storing a data structure for displaying a Unified Modeling Language (UML) model of behavior of a network of computing devices, the behavior dependent on location of the computing devices, comprising: creating a UML model comprising a plurality of line charts arranged in parallel to a time indicating axis, adding to the displayed UML model a plurality of connectors indicating communication between two entities, where a distance between each two line charts of the plurality of line charts in the UML model maps a geographical distance therebetween, and wherein the distance and the value and the plurality of connectors are editable according to at least one user input indicative of a selection of an area of the displayed UML model.

Abstract: Embodiments of the present invention may provide the capability to identify a specific object being interacted with that may be cheaply and easily included in mass-produced objects. In an embodiment, a computer-implemented method for object identification may comprise receiving a signal produced by a physical interaction with an object to be identified, the signal produced by an identification structure coupled to the object during physical interaction with the object, processing the signal to form digital data identifying the object, and accessing a database using the digital data to retrieve additional information identifying or describing properties of the object identified.

Abstract: A computer implemented method, a computerized system and a computer program product for anomaly classification. The computer implemented method comprises obtaining a data set, wherein the data set comprises a plurality of data points. The method further comprises filtering the data set based on an absolute distance criterion and performing anomaly classification on a test data point of the data set, wherein the anomaly classification is based on a relative density criterion. The method further comprises outputting an outcome of the anomaly classification.

Abstract: Method, apparatus and product for utilizing stochastic controller to provide user-controlled notification rate of wearable-based events. The method comprises obtaining events issued by a module based on analysis of multiple sensor readings of one or more sensors of a wearable device. The method further comprises determining by a stochastic controller whether to provide an alert to a user based on the events and based on a user preference, wherein the user preference is indicative of a desired notification rate of the user, wherein the stochastic controller comprises a stochastic model of an environment. Based on such determination, alerts are outputted to the user.

Abstract: A computer-implemented method, computerized apparatus and computer program product, the method comprising: obtaining data measured by one or more sensors; segmenting the data into a plurality of sliding windows; extracting one or more features from each of the plurality of sliding windows; analyzing, by a machine learning process, the extracted features to determine, for each sliding window, an activity detection in the sliding window; and determining an activity detection result in the data to be positive responsive to activity detection by the machine learning process in at least a number M of sliding windows out of a number N of consecutive sliding windows, wherein M>1.

Abstract: According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving two or more predefined transition probability values of a Markov decision process (MDP) of a control system, where each of the predefined transition probability values is associated with a transition between a first state and a second state, both from two or more system states, resulting from execution of one or more control actions of the control system. The method comprises an action of computing one or more new transition probability values by an analysis of the predefined transition probability values, the system states and the control actions. The method comprises an action of determining one or more recommended control actions for the respective system state based on the new transition probability value.

Abstract: According to some embodiments of the present invention there is provided a method for determining a control action in a control system using a Markov decision process. The method comprises an action of receiving measured transition probability values of a Markov decision process (MDP) and receiving simulated transition probability values generated by performing a control system simulation. New transition probability values are computed by calculating a measured data count of some of the sensor measurements and a simulated data count of some of the simulated transition data. New transition probability values are computed from a weighted average between the measured transition probability values and the simulated transition probability values using the measured data count and the simulated data count. A new control action is determined based on the one or more new transition probability value.

Abstract: A method for wastewater treatment that comprises receiving influent readings from sensors located along influent stream(s) of a wastewater treatment unit, effluent readings from sensors located along effluent stream(s) of the wastewater treatment unit, a feedback flow variable calculated according to a state of a feedback flow channel between an effluent output and an influent input, analyzing the influent readings and the effluent readings to extract an influent flow variable, a total nitrogen at effluent variable and a total phosphorus at effluent variable, and calculating control instructions to control the wastewater treatment unit by assigning a combination of a cost variable reflecting a treatment cost for treating the influent stream(s), a time period, the influent flow variable, the total nitrogen at effluent variable, the total phosphorus at effluent variable, and the feedback flow variable in a state space of the wastewater treatment unit.

Abstract: Data storage management by determining, for leaf and summary storage spaces of a data storage space hierarchy having at least two leaf storage spaces descending from at least one summary storage space, an invariant leaf attribute value for each leaf attribute type, an invariant summary attribute value for each descending leaf attribute type as a sum of the invariant leaf attribute values of all leaves descending from the summary storage space, and for each leaf, a variable leaf attribute value for each leaf attribute type, and, for each summary storage space, a variable summary attribute value for each descending leaf attribute type, where for each summary storage space, and for each storage space immediately descending from the summary storage space, each variable leaf attribute value of the immediately descending storage space is expressed as a proportion of the variable summary attribute value for the same attribute type.

Abstract: A method for determining a variable near-optimal policy for a problem formulated as Markov Decision Process, the problem comprising at least one limited action entry, the limited action entry being an entry of an action of a finite set of actions limited in the number of times its value may be changed, the method comprising using at least one hardware processor for: receiving data elements with respect to the problem, the data elements comprising: (a) a finite set of states, (b) the finite set of actions, (c) a transition probabilities matrix determining transition probabilities between states of the finite set of states, once actions of the set of actions are performed; (d) an immediate cost function, wherein the value of the immediate cost function is determined for a pair of a state of the finite set of states and an action of the finite set of actions, and (e) a discount factor; updating one or more data elements of the received data elements relating to the at least one limited action entry, wherein the

Abstract: A novel and useful system and method of decentralized decision-making for real-time scheduling in a multi-process environment. For each process step and/or resource capable of processing a particular step, a service index is calculated. The calculation takes into account several measures, such as business level measures, operational measures and employee level measure. The decision of which process step a resource should next work on or what step to assign to a resource is based on the service index calculation and, optionally, other production factors. In one embodiment, the resource is assigned the process step with the maximal service index. Alternatively, when a resource becomes available, all process steps the resource is capable of processing are presented in order of descending service index. The resource then selects which process step to work on next.

Abstract: Supporting problem resolution of an organization, in one aspect, may include obtaining operational data associated with the organization, calculating operating metrics based on the operational data, detecting one or more metrics trends based on the calculated operational metrics, identifying one or more relations between the metric trends, and determining one or more SEM patterns from two or more of the calculated operational metrics and metric trends.

Abstract: Supporting problem resolution of an organization, in one aspect, may include obtaining operational data associated with the organization, calculating operating metrics based on the operational data, detecting one or more metrics trends based on the calculated operational metrics, identifying one or more relations between the metric trends, and determining one or more SEM patterns from two or more of the calculated operational metrics and metric trends.